Instantaneous network model diagnostics - balanced statistics

This file shows diagnostics for instantaneous network models fit using balanced racial/ethnic mixing matrices and degree terms adjusted to correspond to the balanced mixing matrices. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.

Load packages and model fits

rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")
## Loading required package: lattice
## Loading required package: RColorBrewer
library("knitr")
library("kableExtra")

load(file = "/homes/dpwhite/R/GitHub Repos/WHAMP/Model fits and simulations/Fit tests and debugging/est/fit.i.buildup.bal.rda")

Model terms and control settings

Model terms and target statistics
Terms Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
edges 479.2 479.2 479.2 479.2 479.2 479.2 479.2 479.2
nodefactor.deg.main.deg.pers.0.1 NA NA NA 172.3 172.3 172.3 172.3 172.3
nodefactor.deg.main.deg.pers.0.2 NA NA NA 36.4 36.4 36.4 36.4 36.4
nodefactor.deg.main.deg.pers.1.0 NA NA NA 38.0 38.0 38.0 38.0 38.0
nodefactor.deg.main.deg.pers.1.1 NA NA NA 135.5 135.5 135.5 135.5 135.5
nodefactor.deg.main.deg.pers.1.2 NA NA NA 145.4 145.4 145.4 145.4 145.4
nodefactor.riskg.O1 NA NA NA NA NA NA 0.4 0.4
nodefactor.riskg.O2 NA NA NA NA NA NA 0.4 0.4
nodefactor.riskg.O3 NA NA NA NA NA NA 6.9 6.9
nodefactor.riskg.O4 NA NA NA NA NA NA 109.5 109.5
nodefactor.riskg.Y1 NA NA NA NA NA NA 1.3 1.3
nodefactor.riskg.Y2 NA NA NA NA NA NA 8.2 8.2
nodefactor.riskg.Y3 NA NA NA NA NA NA 70.8 70.8
nodefactor.race..wa.B NA 75.6 75.6 75.6 75.6 75.6 75.6 75.6
nodefactor.race..wa.H NA 149.2 149.2 149.2 149.2 149.2 149.2 149.2
nodefactor.region.EW NA NA NA NA 83.5 83.5 83.5 83.5
nodefactor.region.OW NA NA NA NA 242.5 242.5 242.5 242.5
nodematch.race..wa.B NA NA 2.5 2.5 2.5 2.5 2.5 2.5
nodematch.race..wa.H NA NA 13.3 13.3 13.3 13.3 13.3 13.3
nodematch.race..wa.O NA NA 286.9 286.9 286.9 286.9 286.9 286.9
nodematch.region NA NA NA NA NA NA NA 383.3
absdiff.sqrt.age NA NA NA NA NA 380.5 380.5 380.5
nodematch.role.class.I -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf
nodematch.role.class.R -Inf -Inf -Inf -Inf -Inf -Inf -Inf -Inf

The control settings for these models are:

set.control.ergm = control.ergm(MCMC.interval = 1e+5,
                                MCMC.samplesize = 7500,
                                MCMC.burnin = 1e+6,
                                MPLE.max.dyad.types = 1e+7,
                                MCMLE.maxit = 400,
                                parallel = np/2, 
                                parallel.type="PSOCK"))

MCMC diagnostics

Model 1

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##           Mean             SD       Naive SE Time-series SE 
##         1.1493        21.8287         0.1260         0.1264 
## 
## 2. Quantiles for each variable:
## 
##     2.5%      25%      50%      75%    97.5% 
## -41.1586 -14.1586   0.8414  15.8414  44.8414 
## 
## 
## Sample statistics cross-correlations:
##       edges
## edges     1
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.001689893
## Lag 2e+05  0.009153440
## Lag 3e+05  0.027073547
## Lag 4e+05 -0.021024930
## Lag 5e+05 -0.021904324
## Chain 2 
##                   edges
## Lag 0      1.000000e+00
## Lag 1e+05 -4.281928e-05
## Lag 2e+05  1.401588e-02
## Lag 3e+05 -2.204210e-02
## Lag 4e+05 -9.814745e-03
## Lag 5e+05 -8.557038e-03
## Chain 3 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.006017968
## Lag 2e+05  0.008391067
## Lag 3e+05 -0.004462612
## Lag 4e+05  0.007289324
## Lag 5e+05 -0.018245787
## Chain 4 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.003809866
## Lag 2e+05 -0.003660976
## Lag 3e+05  0.010345232
## Lag 4e+05  0.007366993
## Lag 5e+05 -0.005793656
## Chain 5 
##                   edges
## Lag 0      1.000000e+00
## Lag 1e+05 -1.115457e-02
## Lag 2e+05 -1.289364e-02
## Lag 3e+05  5.238162e-05
## Lag 4e+05 -6.304455e-03
## Lag 5e+05 -1.903246e-02
## Chain 6 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.010499933
## Lag 2e+05  0.004685483
## Lag 3e+05  0.004444947
## Lag 4e+05 -0.020608392
## Lag 5e+05  0.005475361
## Chain 7 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05 -0.009310435
## Lag 2e+05  0.004987512
## Lag 3e+05  0.012626092
## Lag 4e+05  0.006809817
## Lag 5e+05 -0.007628960
## Chain 8 
##                  edges
## Lag 0      1.000000000
## Lag 1e+05  0.024097480
## Lag 2e+05  0.002007648
## Lag 3e+05  0.009428801
## Lag 4e+05  0.030299736
## Lag 5e+05 -0.020096243
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##   edges 
## -0.4734 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.6359181 
## Joint P-value (lower = worse):  0.6339186 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## -1.16 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.2462308 
## Joint P-value (lower = worse):  0.2423417 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.5236 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.6005875 
## Joint P-value (lower = worse):  0.6090205 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## 1.186 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.2356807 
## Joint P-value (lower = worse):  0.2090867 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## 1.059 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.2895834 
## Joint P-value (lower = worse):  0.287077 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## 0.1135 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.9096263 
## Joint P-value (lower = worse):  0.9137976 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##  edges 
## -1.107 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.2683499 
## Joint P-value (lower = worse):  0.2564863 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
## edges 
## 1.397 
## 
## Individual P-values (lower = worse):
##     edges 
## 0.1622806 
## Joint P-value (lower = worse):  0.1452099 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 2

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                         Mean     SD Naive SE Time-series SE
## edges                 1.5613 21.872  0.12628        0.12628
## nodefactor.race..wa.B 0.1038  8.988  0.05189        0.05186
## nodefactor.race..wa.H 0.8564 13.134  0.07583        0.07537
## 
## 2. Quantiles for each variable:
## 
##                         2.5%     25%    50%    75% 97.5%
## edges                 -41.16 -13.159 1.8414 15.841 44.84
## nodefactor.race..wa.B -16.59  -5.591 0.4092  6.409 18.41
## nodefactor.race..wa.H -24.17  -8.174 0.8261  9.826 26.83
## 
## 
## Sample statistics cross-correlations:
##                           edges nodefactor.race..wa.B
## edges                 1.0000000            0.38441849
## nodefactor.race..wa.B 0.3844185            1.00000000
## nodefactor.race..wa.H 0.5154821            0.09685207
##                       nodefactor.race..wa.H
## edges                            0.51548213
## nodefactor.race..wa.B            0.09685207
## nodefactor.race..wa.H            1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.009022949          -0.015724055          -0.002773037
## Lag 2e+05  0.009469770           0.017596899           0.012509957
## Lag 3e+05  0.010087765          -0.007695397          -0.009495744
## Lag 4e+05 -0.039240477          -0.020216079           0.006226732
## Lag 5e+05 -0.013166712          -0.002460691           0.003743976
## Chain 2 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05 -0.0053239317           0.027962512           0.012492868
## Lag 2e+05  0.0108035530           0.009711324           0.018945139
## Lag 3e+05  0.0004870424          -0.049566267           0.016802157
## Lag 4e+05  0.0033195249          -0.021129858          -0.011135839
## Lag 5e+05  0.0248522163           0.019676352          -0.004475621
## Chain 3 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.015922782           0.006799147          -0.026426875
## Lag 2e+05 -0.018077562          -0.009641266          -0.023913972
## Lag 3e+05 -0.016861808          -0.022274445          -0.002953678
## Lag 4e+05  0.009089872           0.014254117           0.006401110
## Lag 5e+05 -0.002920697          -0.021227343          -0.009534556
## Chain 4 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05 -0.02077977          -0.002784020          -0.014684571
## Lag 2e+05  0.01113820           0.002358376           0.006931872
## Lag 3e+05 -0.01805143          -0.008072311           0.013298663
## Lag 4e+05  0.01095108           0.006996996          -0.013841419
## Lag 5e+05 -0.02274142           0.011105677          -0.007316140
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.010753978           0.009831448           0.018405194
## Lag 2e+05  0.010881844          -0.007814431          -0.001866209
## Lag 3e+05  0.002233129          -0.009914092           0.032383537
## Lag 4e+05 -0.021256695          -0.003659570           0.014074036
## Lag 5e+05 -0.005219297           0.007280053           0.030330229
## Chain 6 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.022106774           0.014996200          -0.009845007
## Lag 2e+05  0.014169176          -0.003223020          -0.013981472
## Lag 3e+05  0.004153907          -0.015238669           0.016785326
## Lag 4e+05 -0.014388188          -0.020144997          -0.008849356
## Lag 5e+05 -0.013637473           0.003578938          -0.020984483
## Chain 7 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.00000000          1.0000000000          1.0000000000
## Lag 1e+05  0.01419279         -0.0000173052          0.0197091700
## Lag 2e+05 -0.01472130          0.0120946138         -0.0181859480
## Lag 3e+05 -0.01774480          0.0230643981         -0.0280144140
## Lag 4e+05  0.01012615          0.0254333911          0.0007863171
## Lag 5e+05  0.01506686          0.0124836525          0.0176464577
## Chain 8 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.004152920           0.016560234          -0.009604462
## Lag 2e+05 -0.009282207          -0.004361675          -0.004539907
## Lag 3e+05 -0.003066974           0.012235250           0.004226071
## Lag 4e+05  0.024814229           0.026747454          -0.015784145
## Lag 5e+05  0.001001616          -0.043421193           0.007805233
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                 1.553                 1.010                 1.607 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.1204122             0.3126963             0.1079489 
## Joint P-value (lower = worse):  0.3178617 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               0.31857              -0.21300              -0.03131 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.7500505             0.8313296             0.9750247 
## Joint P-value (lower = worse):  0.96269 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.2685                1.4460               -0.6820 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.7883462             0.1481834             0.4952376 
## Joint P-value (lower = worse):  0.394729 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.5654                2.6781                0.6261 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##           0.117499275           0.007404733           0.531276405 
## Joint P-value (lower = worse):  0.08243981 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.7900               -0.4967                0.4714 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.4295257             0.6193964             0.6373845 
## Joint P-value (lower = worse):  0.6147585 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.0277               -0.1318                1.3061 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.3041104             0.8951456             0.1915026 
## Joint P-value (lower = worse):  0.1473041 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                1.2696               -0.1001                1.1351 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.2042372             0.9202725             0.2563137 
## Joint P-value (lower = worse):  0.4604688 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.3805               -0.8038               -1.4618 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.1674296             0.4215240             0.1437899 
## Joint P-value (lower = worse):  0.4158051 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 3

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                           Mean     SD Naive SE Time-series SE
## edges                  1.24218 21.854 0.126174       0.125377
## nodefactor.race..wa.B  0.10385  8.964 0.051751       0.051740
## nodefactor.race..wa.H  0.97515 13.246 0.076478       0.074811
## nodematch.race..wa.B   0.02838  1.591 0.009183       0.009184
## nodematch.race..wa.H  -0.05602  3.610 0.020840       0.020637
## nodematch.race..wa.O   0.08918 16.875 0.097426       0.098370
## 
## 2. Quantiles for each variable:
## 
##                          2.5%     25%     50%    75%  97.5%
## edges                 -41.159 -13.159  0.8414 15.841 44.841
## nodefactor.race..wa.B -16.591  -5.591  0.4092  6.409 18.409
## nodefactor.race..wa.H -24.174  -8.174  0.8261  9.826 27.826
## nodematch.race..wa.B   -2.540  -1.540 -0.5399  1.460  3.460
## nodematch.race..wa.H   -6.269  -2.269 -0.2690  2.731  7.731
## nodematch.race..wa.O  -32.880 -11.880  0.1200 11.120 33.120
## 
## 
## Sample statistics cross-correlations:
##                            edges nodefactor.race..wa.B
## edges                 1.00000000           0.393068225
## nodefactor.race..wa.B 0.39306822           1.000000000
## nodefactor.race..wa.H 0.51386231           0.147507901
## nodematch.race..wa.B  0.06211785           0.347555220
## nodematch.race..wa.H  0.16775075           0.007447026
## nodematch.race..wa.O  0.77006663           0.004947318
##                       nodefactor.race..wa.H nodematch.race..wa.B
## edges                           0.513862311          0.062117851
## nodefactor.race..wa.B           0.147507901          0.347555220
## nodefactor.race..wa.H           1.000000000         -0.014974825
## nodematch.race..wa.B           -0.014974825          1.000000000
## nodematch.race..wa.H            0.549543147         -0.000261336
## nodematch.race..wa.O           -0.006231405          0.001294147
##                       nodematch.race..wa.H nodematch.race..wa.O
## edges                         0.1677507475         0.7700666330
## nodefactor.race..wa.B         0.0074470259         0.0049473181
## nodefactor.race..wa.H         0.5495431466        -0.0062314054
## nodematch.race..wa.B         -0.0002613360         0.0012941467
## nodematch.race..wa.H          1.0000000000        -0.0007619151
## nodematch.race..wa.O         -0.0007619151         1.0000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.026024014          -0.007861391          -0.018745335
## Lag 2e+05  0.010972370          -0.016940120           0.002058190
## Lag 3e+05  0.006346110           0.001170770           0.013354741
## Lag 4e+05  0.011008592          -0.015434240           0.027563978
## Lag 5e+05 -0.009772797          -0.036919251           0.008208136
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000         1.0000000000          1.000000000
## Lag 1e+05        -0.0008005827        -0.0114541206         -0.028089462
## Lag 2e+05        -0.0128978015         0.0106560330          0.006066513
## Lag 3e+05         0.0099718233        -0.0177363375          0.012666456
## Lag 4e+05         0.0070236819         0.0096399365          0.043240094
## Lag 5e+05         0.0252195969        -0.0002301732         -0.021047261
## Chain 2 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05  0.013935792           0.008511563           0.017970302
## Lag 2e+05  0.021723465           0.021004695           0.028664223
## Lag 3e+05  0.012197349          -0.011175970          -0.023949698
## Lag 4e+05 -0.005521791           0.025413985          -0.011846986
## Lag 5e+05  0.031675169           0.021346591          -0.005736075
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.000000e+00          1.000000000
## Lag 1e+05          0.003471047        -2.423258e-02          0.007905871
## Lag 2e+05          0.011120527         1.403859e-02         -0.003365791
## Lag 3e+05         -0.008340247        -3.546904e-03          0.035037156
## Lag 4e+05         -0.021522482         9.606818e-06         -0.015396389
## Lag 5e+05         -0.014900792        -1.329879e-02          0.019795714
## Chain 3 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000          1.0000000000           1.000000000
## Lag 1e+05  0.003397993          0.0060004583          -0.005675022
## Lag 2e+05  0.024716748         -0.0001688861           0.022123298
## Lag 3e+05 -0.016269318         -0.0176075680           0.021586456
## Lag 4e+05  0.011334099          0.0087533477          -0.017738368
## Lag 5e+05  0.022205606          0.0088037143           0.008110501
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05         -0.034108446         -0.023250311         0.0008044902
## Lag 2e+05          0.004047861          0.017671998         0.0393916194
## Lag 3e+05          0.009411459          0.008775821        -0.0288122799
## Lag 4e+05          0.001718397          0.010825431         0.0088465087
## Lag 5e+05          0.035321841          0.001819671        -0.0010461830
## Chain 4 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.00000000           1.000000000           1.000000000
## Lag 1e+05  0.01899986          -0.022878875          -0.014343713
## Lag 2e+05 -0.02547010           0.022200519          -0.009866647
## Lag 3e+05 -0.02047446          -0.003285218          -0.019404079
## Lag 4e+05 -0.01556433          -0.023868140           0.015184960
## Lag 5e+05 -0.00135302          -0.012179588           0.006803242
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000          1.000000000          1.000000000
## Lag 1e+05        -0.0208354506         -0.022366455          0.020593395
## Lag 2e+05         0.0226661605         -0.009328364         -0.019323919
## Lag 3e+05        -0.0083598465          0.013827168         -0.008600903
## Lag 4e+05         0.0221293699          0.003358433         -0.011669822
## Lag 5e+05         0.0006011164          0.029316371         -0.003626088
## Chain 5 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.012808393           0.001862343          -0.037829167
## Lag 2e+05 -0.011224363          -0.028467306          -0.008386686
## Lag 3e+05 -0.009118353           0.001797468          -0.030248435
## Lag 4e+05  0.011517309           0.020184801          -0.006456949
## Lag 5e+05  0.010945594           0.002058734           0.005702717
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05          0.005658357          0.005165130         0.0022019931
## Lag 2e+05          0.008933518         -0.004424192        -0.0098330136
## Lag 3e+05          0.004927673         -0.027353627         0.0075251217
## Lag 4e+05          0.005856029         -0.004294367         0.0253837951
## Lag 5e+05          0.011854049         -0.013165580         0.0004862648
## Chain 6 
##                  edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.000000000           1.000000000           1.000000000
## Lag 1e+05 -0.026050767          -0.026819077           0.003480310
## Lag 2e+05  0.008923019           0.003030946          -0.003883918
## Lag 3e+05 -0.001005758          -0.028951427           0.012633259
## Lag 4e+05  0.013174808           0.019301918          -0.020416136
## Lag 5e+05  0.003431148           0.005386935          -0.028325800
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.006577972         -0.031874302         -0.010483285
## Lag 2e+05          0.006504528          0.011905147          0.007888239
## Lag 3e+05          0.013913778          0.011087834          0.002745668
## Lag 4e+05          0.012330248         -0.010013670          0.009047372
## Lag 5e+05         -0.025398403         -0.006265385         -0.017007644
## Chain 7 
##                   edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0      1.0000000000           1.000000000           1.000000000
## Lag 1e+05  0.0126900790           0.023438634           0.013476453
## Lag 2e+05 -0.0115051782          -0.016919763          -0.001146344
## Lag 3e+05  0.0095044435           0.002674901           0.005898297
## Lag 4e+05  0.0067431482           0.014535407          -0.004917430
## Lag 5e+05  0.0001885166          -0.011969812           0.014887413
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000          1.000000000
## Lag 1e+05          0.032731885        -0.0086217183         -0.005945039
## Lag 2e+05         -0.000234731        -0.0019192373          0.001236863
## Lag 3e+05         -0.003190540        -0.0003931676          0.007358085
## Lag 4e+05         -0.002904919        -0.0011007239         -0.014833666
## Lag 5e+05         -0.023471044         0.0001542197         -0.005942981
## Chain 8 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0     1.000000000           1.000000000          1.0000000000
## Lag 1e+05 0.012009216           0.015176946          0.0012529780
## Lag 2e+05 0.007658820          -0.008131660         -0.0002199334
## Lag 3e+05 0.003108993          -0.001200120         -0.0025650908
## Lag 4e+05 0.004779292          -0.006300082         -0.0044060517
## Lag 5e+05 0.026567282           0.001779794         -0.0032720252
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000          1.000000000
## Lag 1e+05          0.008926717        -0.0125749787         -0.002028612
## Lag 2e+05          0.025454993        -0.0244431110         -0.007979207
## Lag 3e+05         -0.013861312         0.0243782442         -0.001393803
## Lag 4e+05         -0.002014802         0.0004982371          0.025947652
## Lag 5e+05         -0.042091889         0.0002028151          0.023182211
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.2619                0.6713               -0.1615 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -0.2446               -1.0942               -0.5758 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.7933912             0.5020141             0.8717109 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.8067607             0.2738455             0.5647508 
## Joint P-value (lower = worse):  0.8753228 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               0.02732              -0.81751              -0.91368 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               0.42574               0.07231               1.04802 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.9782080             0.4136370             0.3608859 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.6702940             0.9423546             0.2946291 
## Joint P-value (lower = worse):  0.7453654 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.5289                0.4494                0.3370 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.4421                1.0338                0.5587 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.5969072             0.6531659             0.7361159 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.6584065             0.3012362             0.5763911 
## Joint P-value (lower = worse):  0.9130372 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##              -0.02998               1.21873              -0.40860 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##              -0.11535              -0.38237              -0.02810 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.9760818             0.2229449             0.6828357 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.9081705             0.7021851             0.9775808 
## Joint P-value (lower = worse):  0.7643018 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.1795                1.3251                0.2671 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.1265                0.2028               -0.8297 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.8575714             0.1851321             0.7893596 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.8993374             0.8393110             0.4066920 
## Joint P-value (lower = worse):  0.8283275 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -1.2484               -0.7663               -1.0240 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##               -1.2443               -1.8879               -0.9663 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.2118744             0.4434894             0.3058226 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.2133973             0.0590455             0.3338725 
## Joint P-value (lower = worse):  0.4258426 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##               -0.2676                0.5372                0.3934 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.7980                0.8157               -1.0561 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.7889983             0.5911472             0.6940114 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.4248484             0.4146513             0.2909213 
## Joint P-value (lower = worse):  0.5042826 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##                0.4220                0.5814               -0.2636 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##                0.5092               -0.7183                0.7733 
## 
## Individual P-values (lower = worse):
##                 edges nodefactor.race..wa.B nodefactor.race..wa.H 
##             0.6730129             0.5609594             0.7921174 
##  nodematch.race..wa.B  nodematch.race..wa.H  nodematch.race..wa.O 
##             0.6106132             0.4725897             0.4393279 
## Joint P-value (lower = worse):  0.4797669 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 4

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                                      Mean     SD Naive SE Time-series SE
## edges                             0.68295 22.059 0.127358        0.12699
## nodefactor.deg.main.deg.pers.0.1 -0.07174 14.379 0.083016        0.08452
## nodefactor.deg.main.deg.pers.0.2  0.20720  6.154 0.035533        0.03553
## nodefactor.deg.main.deg.pers.1.0  0.07756  6.314 0.036452        0.03769
## nodefactor.deg.main.deg.pers.1.1  0.43554 12.457 0.071921        0.07168
## nodefactor.deg.main.deg.pers.1.2  0.06271 13.043 0.075305        0.07483
## nodefactor.race..wa.B             0.08848  8.992 0.051916        0.05164
## nodefactor.race..wa.H             0.45065 13.267 0.076600        0.07794
## nodematch.race..wa.B              0.05815  1.608 0.009286        0.00928
## nodematch.race..wa.H              0.16191  3.631 0.020963        0.02088
## nodematch.race..wa.O              0.48241 17.043 0.098395        0.09899
## 
## 2. Quantiles for each variable:
## 
##                                     2.5%     25%      50%    75%  97.5%
## edges                            -42.159 -14.159  0.84138 15.841 44.841
## nodefactor.deg.main.deg.pers.0.1 -27.310 -10.310 -0.31004  9.690 28.690
## nodefactor.deg.main.deg.pers.0.2 -11.371  -4.371 -0.37103  4.629 12.629
## nodefactor.deg.main.deg.pers.1.0 -12.033  -4.033 -0.03347  3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -23.538  -8.538  0.46214  8.462 25.462
## nodefactor.deg.main.deg.pers.1.2 -24.388  -9.388 -0.38812  8.612 25.637
## nodefactor.race..wa.B            -16.591  -6.591 -0.59082  6.409 18.409
## nodefactor.race..wa.H            -25.174  -8.174 -0.17392  8.826 26.826
## nodematch.race..wa.B              -2.540  -1.540 -0.53985  1.460  3.460
## nodematch.race..wa.H              -6.269  -2.269 -0.26902  2.731  7.731
## nodematch.race..wa.O             -31.880 -10.880  0.11998 12.120 34.120
## 
## 
## Sample statistics cross-correlations:
##                                       edges
## edges                            1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.55667565
## nodefactor.deg.main.deg.pers.0.2 0.27027438
## nodefactor.deg.main.deg.pers.1.0 0.27391605
## nodefactor.deg.main.deg.pers.1.1 0.50227563
## nodefactor.deg.main.deg.pers.1.2 0.51557211
## nodefactor.race..wa.B            0.38787191
## nodefactor.race..wa.H            0.51437157
## nodematch.race..wa.B             0.07608636
## nodematch.race..wa.H             0.16598277
## nodematch.race..wa.O             0.77665651
##                                  nodefactor.deg.main.deg.pers.0.1
## edges                                                  0.55667565
## nodefactor.deg.main.deg.pers.0.1                       1.00000000
## nodefactor.deg.main.deg.pers.0.2                       0.08145035
## nodefactor.deg.main.deg.pers.1.0                       0.06615908
## nodefactor.deg.main.deg.pers.1.1                       0.14595394
## nodefactor.deg.main.deg.pers.1.2                       0.14454351
## nodefactor.race..wa.B                                  0.22411727
## nodefactor.race..wa.H                                  0.27639304
## nodematch.race..wa.B                                   0.04018601
## nodematch.race..wa.H                                   0.08330158
## nodematch.race..wa.O                                   0.43445295
##                                  nodefactor.deg.main.deg.pers.0.2
## edges                                                  0.27027438
## nodefactor.deg.main.deg.pers.0.1                       0.08145035
## nodefactor.deg.main.deg.pers.0.2                       1.00000000
## nodefactor.deg.main.deg.pers.1.0                       0.03757093
## nodefactor.deg.main.deg.pers.1.1                       0.06484824
## nodefactor.deg.main.deg.pers.1.2                       0.07242332
## nodefactor.race..wa.B                                  0.09053553
## nodefactor.race..wa.H                                  0.12387700
## nodematch.race..wa.B                                   0.01228321
## nodematch.race..wa.H                                   0.02982323
## nodematch.race..wa.O                                   0.22439250
##                                  nodefactor.deg.main.deg.pers.1.0
## edges                                                  0.27391605
## nodefactor.deg.main.deg.pers.0.1                       0.06615908
## nodefactor.deg.main.deg.pers.0.2                       0.03757093
## nodefactor.deg.main.deg.pers.1.0                       1.00000000
## nodefactor.deg.main.deg.pers.1.1                       0.07135700
## nodefactor.deg.main.deg.pers.1.2                       0.07520253
## nodefactor.race..wa.B                                  0.08984153
## nodefactor.race..wa.H                                  0.16496842
## nodematch.race..wa.B                                   0.00735322
## nodematch.race..wa.H                                   0.06412388
## nodematch.race..wa.O                                   0.20476023
##                                  nodefactor.deg.main.deg.pers.1.1
## edges                                                  0.50227563
## nodefactor.deg.main.deg.pers.0.1                       0.14595394
## nodefactor.deg.main.deg.pers.0.2                       0.06484824
## nodefactor.deg.main.deg.pers.1.0                       0.07135700
## nodefactor.deg.main.deg.pers.1.1                       1.00000000
## nodefactor.deg.main.deg.pers.1.2                       0.13409180
## nodefactor.race..wa.B                                  0.16857777
## nodefactor.race..wa.H                                  0.27579884
## nodematch.race..wa.B                                   0.03705199
## nodematch.race..wa.H                                   0.09782362
## nodematch.race..wa.O                                   0.39038346
##                                  nodefactor.deg.main.deg.pers.1.2
## edges                                                  0.51557211
## nodefactor.deg.main.deg.pers.0.1                       0.14454351
## nodefactor.deg.main.deg.pers.0.2                       0.07242332
## nodefactor.deg.main.deg.pers.1.0                       0.07520253
## nodefactor.deg.main.deg.pers.1.1                       0.13409180
## nodefactor.deg.main.deg.pers.1.2                       1.00000000
## nodefactor.race..wa.B                                  0.17989812
## nodefactor.race..wa.H                                  0.31078883
## nodematch.race..wa.B                                   0.02961878
## nodematch.race..wa.H                                   0.11322877
## nodematch.race..wa.O                                   0.38285480
##                                  nodefactor.race..wa.B
## edges                                      0.387871906
## nodefactor.deg.main.deg.pers.0.1           0.224117270
## nodefactor.deg.main.deg.pers.0.2           0.090535532
## nodefactor.deg.main.deg.pers.1.0           0.089841530
## nodefactor.deg.main.deg.pers.1.1           0.168577766
## nodefactor.deg.main.deg.pers.1.2           0.179898116
## nodefactor.race..wa.B                      1.000000000
## nodefactor.race..wa.H                      0.141905467
## nodematch.race..wa.B                       0.349311339
## nodematch.race..wa.H                      -0.002678008
## nodematch.race..wa.O                       0.007065495
##                                  nodefactor.race..wa.H
## edges                                      0.514371569
## nodefactor.deg.main.deg.pers.0.1           0.276393039
## nodefactor.deg.main.deg.pers.0.2           0.123876998
## nodefactor.deg.main.deg.pers.1.0           0.164968425
## nodefactor.deg.main.deg.pers.1.1           0.275798837
## nodefactor.deg.main.deg.pers.1.2           0.310788826
## nodefactor.race..wa.B                      0.141905467
## nodefactor.race..wa.H                      1.000000000
## nodematch.race..wa.B                       0.003790146
## nodematch.race..wa.H                       0.551340709
## nodematch.race..wa.O                       0.003291703
##                                  nodematch.race..wa.B nodematch.race..wa.H
## edges                                    0.0760863630         0.1659827659
## nodefactor.deg.main.deg.pers.0.1         0.0401860130         0.0833015834
## nodefactor.deg.main.deg.pers.0.2         0.0122832142         0.0298232306
## nodefactor.deg.main.deg.pers.1.0         0.0073532203         0.0641238797
## nodefactor.deg.main.deg.pers.1.1         0.0370519944         0.0978236193
## nodefactor.deg.main.deg.pers.1.2         0.0296187846         0.1132287721
## nodefactor.race..wa.B                    0.3493113387        -0.0026780076
## nodefactor.race..wa.H                    0.0037901456         0.5513407095
## nodematch.race..wa.B                     1.0000000000         0.0009021849
## nodematch.race..wa.H                     0.0009021849         1.0000000000
## nodematch.race..wa.O                     0.0038975379        -0.0001963725
##                                  nodematch.race..wa.O
## edges                                    0.7766565131
## nodefactor.deg.main.deg.pers.0.1         0.4344529522
## nodefactor.deg.main.deg.pers.0.2         0.2243925038
## nodefactor.deg.main.deg.pers.1.0         0.2047602322
## nodefactor.deg.main.deg.pers.1.1         0.3903834633
## nodefactor.deg.main.deg.pers.1.2         0.3828547995
## nodefactor.race..wa.B                    0.0070654945
## nodefactor.race..wa.H                    0.0032917029
## nodematch.race..wa.B                     0.0038975379
## nodematch.race..wa.H                    -0.0001963725
## nodematch.race..wa.O                     1.0000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                     1.0000000000
## Lag 1e+05  0.018666330                    -0.0037759378
## Lag 2e+05  0.005739188                     0.0077095307
## Lag 3e+05  0.017224828                     0.0003097942
## Lag 4e+05  0.022019981                     0.0203778431
## Lag 5e+05 -0.024417255                    -0.0312957186
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0011957233
## Lag 2e+05                    -0.0004280329
## Lag 3e+05                    -0.0249984898
## Lag 4e+05                     0.0052368492
## Lag 5e+05                     0.0066207407
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.011665434
## Lag 2e+05                      0.015369456
## Lag 3e+05                     -0.006222952
## Lag 4e+05                     -0.010995256
## Lag 5e+05                     -0.002979721
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.015761516
## Lag 2e+05                     -0.021638176
## Lag 3e+05                      0.007410929
## Lag 4e+05                      0.008875459
## Lag 5e+05                     -0.005338917
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.011218738          -0.009124160
## Lag 2e+05                     -0.006965569          -0.010365712
## Lag 3e+05                     -0.026602935           0.010689471
## Lag 4e+05                      0.013076452           0.025843576
## Lag 5e+05                      0.002117657           0.005826428
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.010467194          0.005059743          0.009542299
## Lag 2e+05           0.014719706         -0.024439009          0.005104634
## Lag 3e+05          -0.001571007         -0.004196889          0.017026564
## Lag 4e+05          -0.008995311         -0.003190505          0.002841721
## Lag 5e+05           0.001753801          0.009162285         -0.013640010
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05          0.024741628
## Lag 2e+05          0.004465142
## Lag 3e+05          0.036456540
## Lag 4e+05          0.012480362
## Lag 5e+05         -0.028799367
## Chain 2 
##                   edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.0000000000                      1.000000000
## Lag 1e+05 -0.0245112596                     -0.015535619
## Lag 2e+05  0.0072594061                     -0.009627415
## Lag 3e+05  0.0031346889                     -0.017858062
## Lag 4e+05 -0.0002524387                     -0.015696317
## Lag 5e+05  0.0245905609                      0.015873217
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.011760749
## Lag 2e+05                      0.007954243
## Lag 3e+05                     -0.035526524
## Lag 4e+05                     -0.001058418
## Lag 5e+05                      0.004657155
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0005029939
## Lag 2e+05                    -0.0319184639
## Lag 3e+05                    -0.0016702182
## Lag 4e+05                     0.0255552738
## Lag 5e+05                    -0.0005405841
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.013445572
## Lag 2e+05                      0.016961685
## Lag 3e+05                     -0.018145050
## Lag 4e+05                      0.004259120
## Lag 5e+05                     -0.005679427
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.004962265          -0.007223569
## Lag 2e+05                      0.011333563           0.008715700
## Lag 3e+05                     -0.031380545          -0.007594706
## Lag 4e+05                      0.005697910           0.005060803
## Lag 5e+05                     -0.019587253          -0.007478605
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05           0.032039999        -0.0330041403          0.023877789
## Lag 2e+05          -0.035907483        -0.0118240370         -0.009881347
## Lag 3e+05          -0.010585244         0.0054405855         -0.052200775
## Lag 4e+05          -0.019256209         0.0039377371         -0.011225338
## Lag 5e+05           0.003245098         0.0008000378          0.003171438
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.032921907
## Lag 2e+05          0.039025377
## Lag 3e+05         -0.003687544
## Lag 4e+05          0.015646067
## Lag 5e+05          0.011673086
## Chain 3 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05 -0.014242898                     -0.022798044
## Lag 2e+05 -0.016710212                      0.015749674
## Lag 3e+05 -0.009227847                     -0.006855614
## Lag 4e+05 -0.005643982                      0.002270064
## Lag 5e+05  0.020141324                     -0.009235859
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.000000e+00
## Lag 1e+05                    -3.282863e-02
## Lag 2e+05                    -8.720194e-05
## Lag 3e+05                    -6.570127e-03
## Lag 4e+05                     5.602982e-03
## Lag 5e+05                    -1.402472e-02
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.011110284
## Lag 2e+05                     -0.023554243
## Lag 3e+05                      0.010679749
## Lag 4e+05                      0.017926291
## Lag 5e+05                      0.009951356
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.026839355
## Lag 2e+05                      0.003480059
## Lag 3e+05                      0.015853757
## Lag 4e+05                     -0.003736142
## Lag 5e+05                     -0.010996598
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000          1.0000000000
## Lag 1e+05                     -0.019843598         -0.0075693675
## Lag 2e+05                     -0.031826382          0.0271848003
## Lag 3e+05                     -0.001297367         -0.0061991839
## Lag 4e+05                     -0.035336028          0.0163928492
## Lag 5e+05                      0.005051403          0.0002235371
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05           0.034770221         0.0073102902          0.023200344
## Lag 2e+05           0.001394111         0.0137706210          0.012008222
## Lag 3e+05           0.005598855         0.0150130511         -0.009735507
## Lag 4e+05           0.012252428        -0.0006232159          0.011196082
## Lag 5e+05           0.033866331         0.0063852287          0.012256199
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.010803411
## Lag 2e+05         -0.001649748
## Lag 3e+05          0.013520755
## Lag 4e+05          0.013075908
## Lag 5e+05          0.018760639
## Chain 4 
##                   edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.0000000000                      1.000000000
## Lag 1e+05 -0.0004328436                     -0.003693957
## Lag 2e+05  0.0224527203                     -0.021948894
## Lag 3e+05  0.0178884449                      0.009722417
## Lag 4e+05  0.0015986205                      0.018326052
## Lag 5e+05 -0.0014412580                     -0.016342832
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.013530686
## Lag 2e+05                     -0.005714144
## Lag 3e+05                      0.013785313
## Lag 4e+05                      0.015238548
## Lag 5e+05                      0.014970668
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                           1.00000000
## Lag 1e+05                       0.02042794
## Lag 2e+05                      -0.01031929
## Lag 3e+05                       0.04043121
## Lag 4e+05                      -0.02787066
## Lag 5e+05                      -0.02764741
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.010194717
## Lag 2e+05                      0.010143559
## Lag 3e+05                      0.008798206
## Lag 4e+05                      0.009328183
## Lag 5e+05                     -0.022716616
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.023640688          -0.012497109
## Lag 2e+05                      0.001897605          -0.028164082
## Lag 3e+05                      0.008629031          -0.021345583
## Lag 4e+05                      0.019604026           0.006250072
## Lag 5e+05                      0.014309604           0.020331105
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.020061050          0.015511518          0.002648422
## Lag 2e+05          -0.014121742         -0.005763931          0.017602241
## Lag 3e+05          -0.001171554         -0.022184840          0.002914685
## Lag 4e+05           0.009695251         -0.016335285         -0.014353659
## Lag 5e+05           0.020461146          0.001655770          0.023073279
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.002048927
## Lag 2e+05          0.001303902
## Lag 3e+05         -0.006224714
## Lag 4e+05         -0.021818495
## Lag 5e+05          0.002785844
## Chain 5 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05 -0.007375889                     -0.013635213
## Lag 2e+05  0.002569697                      0.002840754
## Lag 3e+05 -0.007854194                     -0.018822130
## Lag 4e+05 -0.006814368                      0.008843009
## Lag 5e+05  0.016207256                      0.014326501
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0020985566
## Lag 2e+05                     0.0331060504
## Lag 3e+05                    -0.0003591816
## Lag 4e+05                    -0.0042162793
## Lag 5e+05                     0.0158837151
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0177174264
## Lag 2e+05                     0.0043742567
## Lag 3e+05                     0.0141739261
## Lag 4e+05                     0.0164211494
## Lag 5e+05                    -0.0004675339
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0008935991
## Lag 2e+05                     0.0088647330
## Lag 3e+05                     0.0055317967
## Lag 4e+05                     0.0082241411
## Lag 5e+05                    -0.0062602582
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.006773484          -0.003412259
## Lag 2e+05                      0.028863504           0.001529638
## Lag 3e+05                      0.015426932          -0.002066278
## Lag 4e+05                     -0.035930686          -0.007378281
## Lag 5e+05                      0.034783896          -0.016970582
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.029746695         -0.008705438          0.021216540
## Lag 2e+05           0.008070614         -0.005622164         -0.005283156
## Lag 3e+05          -0.007824687         -0.016565797         -0.002746903
## Lag 4e+05           0.026015403          0.025684496          0.018510554
## Lag 5e+05           0.025475139         -0.002655474          0.008665309
##           nodematch.race..wa.O
## Lag 0              1.000000000
## Lag 1e+05         -0.015537497
## Lag 2e+05          0.011577057
## Lag 3e+05         -0.008209481
## Lag 4e+05         -0.025734770
## Lag 5e+05          0.017956338
## Chain 6 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.011974449                      0.004156950
## Lag 2e+05 -0.011950848                     -0.008760281
## Lag 3e+05  0.025998785                      0.002936549
## Lag 4e+05 -0.001193861                     -0.009729118
## Lag 5e+05  0.029922918                      0.055354895
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.008143697
## Lag 2e+05                     -0.021005816
## Lag 3e+05                      0.009107715
## Lag 4e+05                      0.011070285
## Lag 5e+05                      0.008287489
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                           1.00000000
## Lag 1e+05                       0.02920826
## Lag 2e+05                      -0.01681149
## Lag 3e+05                       0.03504881
## Lag 4e+05                      -0.01132099
## Lag 5e+05                      -0.01747777
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.019134263
## Lag 2e+05                      0.004273317
## Lag 3e+05                      0.029547947
## Lag 4e+05                      0.013088244
## Lag 5e+05                      0.007601734
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                         1.0000000000           1.000000000
## Lag 1e+05                     0.0127845399          -0.012547352
## Lag 2e+05                     0.0164301192          -0.001416912
## Lag 3e+05                     0.0020997803          -0.012728526
## Lag 4e+05                     0.0002528062           0.003238465
## Lag 5e+05                    -0.0318521626           0.005730140
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.026912185          0.002102384          0.019439430
## Lag 2e+05           0.013584014         -0.017637383          0.002864335
## Lag 3e+05           0.032814889         -0.007272760          0.006097200
## Lag 4e+05           0.017669839         -0.030022546          0.026785752
## Lag 5e+05           0.005965472         -0.018038929         -0.002212079
##           nodematch.race..wa.O
## Lag 0             1.000000e+00
## Lag 1e+05         9.096203e-05
## Lag 2e+05        -2.256691e-02
## Lag 3e+05         2.099497e-03
## Lag 4e+05        -1.550796e-02
## Lag 5e+05         2.593182e-02
## Chain 7 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05 -0.019767150                     -0.005024693
## Lag 2e+05 -0.001151465                      0.029787954
## Lag 3e+05  0.005519589                      0.030181888
## Lag 4e+05 -0.018130627                     -0.022831583
## Lag 5e+05  0.006745333                      0.017784850
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.007417689
## Lag 2e+05                     -0.009973025
## Lag 3e+05                     -0.019231361
## Lag 4e+05                      0.004947851
## Lag 5e+05                      0.008159084
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                           1.00000000
## Lag 1e+05                      -0.01156438
## Lag 2e+05                       0.01630705
## Lag 3e+05                       0.01560482
## Lag 4e+05                      -0.01431009
## Lag 5e+05                       0.03181561
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.003912581
## Lag 2e+05                      0.006818850
## Lag 3e+05                      0.015476021
## Lag 4e+05                     -0.013224579
## Lag 5e+05                      0.016211170
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000            1.00000000
## Lag 1e+05                     -0.039018433           -0.01279031
## Lag 2e+05                      0.011111121            0.01931533
## Lag 3e+05                     -0.023793937            0.02178275
## Lag 4e+05                      0.030590710           -0.00103793
## Lag 5e+05                      0.003704081            0.02773055
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05          -0.023009521         -0.003980853        -0.0081311029
## Lag 2e+05          -0.006578769          0.023155173         0.0074280089
## Lag 3e+05          -0.026202325         -0.008369137         0.0004053863
## Lag 4e+05          -0.003029024         -0.003426198        -0.0209832256
## Lag 5e+05           0.001231233         -0.002850688        -0.0115765579
##           nodematch.race..wa.O
## Lag 0               1.00000000
## Lag 1e+05          -0.03079417
## Lag 2e+05          -0.01984096
## Lag 3e+05           0.01315799
## Lag 4e+05          -0.01119661
## Lag 5e+05          -0.00588272
## Chain 8 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.013385862                      0.002912298
## Lag 2e+05  0.006714281                      0.033986965
## Lag 3e+05 -0.001097486                     -0.012346889
## Lag 4e+05  0.013835183                      0.010791033
## Lag 5e+05 -0.010402714                     -0.015808080
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0073700831
## Lag 2e+05                     0.0185889334
## Lag 3e+05                    -0.0033464658
## Lag 4e+05                     0.0001437143
## Lag 5e+05                     0.0276944798
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0017742096
## Lag 2e+05                     0.0158863330
## Lag 3e+05                     0.0569137145
## Lag 4e+05                    -0.0006681401
## Lag 5e+05                     0.0485637811
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0058893420
## Lag 2e+05                     0.0108751046
## Lag 3e+05                    -0.0003538409
## Lag 4e+05                    -0.0119199434
## Lag 5e+05                    -0.0036520360
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000            1.00000000
## Lag 1e+05                     -0.007245302           -0.01139455
## Lag 2e+05                     -0.047578111           -0.03460990
## Lag 3e+05                      0.003336066           -0.01150279
## Lag 4e+05                     -0.025807617           -0.01784352
## Lag 5e+05                     -0.015344533            0.02317056
##           nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.0000000000          1.000000000           1.00000000
## Lag 1e+05          0.0018551123          0.025624335          -0.01644295
## Lag 2e+05          0.0143060945          0.015216757           0.03663161
## Lag 3e+05         -0.0030124521          0.011623074          -0.04272490
## Lag 4e+05          0.0187610937         -0.006498567           0.01783758
## Lag 5e+05         -0.0008851809          0.014396257          -0.01490497
##           nodematch.race..wa.O
## Lag 0             1.000000e+00
## Lag 1e+05         1.598251e-02
## Lag 2e+05        -2.234496e-03
## Lag 3e+05         1.968089e-02
## Lag 4e+05         9.085944e-05
## Lag 5e+05        -1.648389e-03
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -0.2535                          -1.4789 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -1.2099                           0.9566 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           1.1378                           0.6199 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          -1.6236                          -0.6300 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          -0.3842                           0.2364 
##             nodematch.race..wa.O 
##                           0.4723 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.7999128                        0.1391555 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.2263242                        0.3387624 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.2552118                        0.5353449 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.1044644                        0.5286838 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                        0.7008285                        0.8130885 
##             nodematch.race..wa.O 
##                        0.6367055 
## Joint P-value (lower = worse):  0.4049708 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.29713                         -0.51497 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          0.04106                          1.52874 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.25266                         -0.80829 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         -0.32414                         -0.67699 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          0.79681                         -1.19253 
##             nodematch.race..wa.O 
##                          0.08736 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.7663650                        0.6065706 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.9672476                        0.1263297 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.8005308                        0.4189240 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.7458317                        0.4984121 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                        0.4255612                        0.2330529 
##             nodematch.race..wa.O 
##                        0.9303840 
## Joint P-value (lower = worse):  0.9138253 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -2.8839                          -1.5541 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -0.4671                          -2.2503 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.8571                          -1.6660 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          -2.8594                          -1.4002 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          -0.4413                           0.7252 
##             nodematch.race..wa.O 
##                          -1.5771 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                      0.003927771                      0.120150745 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                      0.640448186                      0.024431939 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                      0.391397115                      0.095716519 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                      0.004244877                      0.161455196 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                      0.658987105                      0.468331735 
##             nodematch.race..wa.O 
##                      0.114778603 
## Joint P-value (lower = worse):  0.1442254 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           0.3431                           0.6316 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           0.7348                          -0.5716 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           0.7755                          -1.2225 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           0.4906                          -0.8383 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                           0.9599                          -0.8720 
##             nodematch.race..wa.O 
##                           1.0689 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.7315430                        0.5276231 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.4624842                        0.5676263 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.4380729                        0.2215238 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.6236932                        0.4018538 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                        0.3371185                        0.3832167 
##             nodematch.race..wa.O 
##                        0.2851043 
## Joint P-value (lower = worse):  0.5681065 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        -0.459831                        -0.383115 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        -0.306132                         0.987571 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         0.135338                        -2.092394 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        -0.546500                        -0.143853 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                         0.862880                         0.147163 
##             nodematch.race..wa.O 
##                        -0.002346 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.64563732                       0.70163461 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.75950441                       0.32336277 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.89234500                       0.03640331 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.58472246                       0.88561681 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.38820332                       0.88300351 
##             nodematch.race..wa.O 
##                       0.99812789 
## Joint P-value (lower = worse):  0.7105389 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -1.9790                          -0.8192 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           0.7815                          -0.5860 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -1.5433                          -1.4738 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          -1.4121                          -1.8985 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          -0.7228                          -1.4823 
##             nodematch.race..wa.O 
##                          -0.9645 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.04781685                       0.41264555 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.43453634                       0.55786822 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.12276291                       0.14052970 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.15792120                       0.05763674 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.46977789                       0.13826571 
##             nodematch.race..wa.O 
##                       0.33479727 
## Joint P-value (lower = worse):  0.6746133 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.07547                          0.78061 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -1.33163                         -1.52421 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.49547                          0.94359 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         -0.41961                         -0.46599 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                         -0.08790                          0.32096 
##             nodematch.race..wa.O 
##                          0.15650 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.9398378                        0.4350314 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.1829833                        0.1274575 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.6202700                        0.3453769 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.6747689                        0.6412222 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                        0.9299525                        0.7482406 
##             nodematch.race..wa.O 
##                        0.8756416 
## Joint P-value (lower = worse):  0.5648157 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         1.160945                        -0.001331 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         0.555878                         1.763473 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         1.449557                         0.041248 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         0.248324                        -0.732046 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                         0.095339                         1.973955 
##             nodematch.race..wa.O 
##                         2.345236 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.24566424                       0.99893785 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.57829458                       0.07782072 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.14718213                       0.96709836 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.80388392                       0.46414025 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.92404567                       0.04838682 
##             nodematch.race..wa.O 
##                       0.01901504 
## Joint P-value (lower = worse):  0.05596303 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 5

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                                     Mean     SD Naive SE Time-series SE
## edges                            0.91405 21.886 0.126357        0.12563
## nodefactor.deg.main.deg.pers.0.1 0.19193 14.216 0.082078        0.08158
## nodefactor.deg.main.deg.pers.0.2 0.08307  6.113 0.035294        0.03530
## nodefactor.deg.main.deg.pers.1.0 0.06006  6.234 0.035994        0.03644
## nodefactor.deg.main.deg.pers.1.1 0.30934 12.390 0.071536        0.07235
## nodefactor.deg.main.deg.pers.1.2 0.29004 13.003 0.075074        0.07490
## nodefactor.race..wa.B            0.19445  8.945 0.051645        0.05191
## nodefactor.race..wa.H            0.44045 13.326 0.076939        0.07677
## nodefactor.region.EW             0.16182  9.432 0.054456        0.05394
## nodefactor.region.OW             1.03306 17.386 0.100379        0.09973
## nodematch.race..wa.B             0.06108  1.617 0.009336        0.00936
## nodematch.race..wa.H             0.25481  3.682 0.021258        0.02110
## nodematch.race..wa.O             0.53524 16.934 0.097768        0.09837
## 
## 2. Quantiles for each variable:
## 
##                                     2.5%     25%      50%    75%  97.5%
## edges                            -41.159 -14.159  0.84138 15.841 43.841
## nodefactor.deg.main.deg.pers.0.1 -27.310  -9.310 -0.31004  9.690 28.690
## nodefactor.deg.main.deg.pers.0.2 -11.371  -4.371 -0.37103  3.629 12.629
## nodefactor.deg.main.deg.pers.1.0 -12.033  -4.033 -0.03347  3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -23.538  -8.538  0.46214  8.462 25.462
## nodefactor.deg.main.deg.pers.1.2 -24.388  -8.388 -0.38812  8.612 26.612
## nodefactor.race..wa.B            -16.591  -5.591  0.40918  6.409 18.409
## nodefactor.race..wa.H            -25.174  -8.174 -0.17392  9.826 26.826
## nodefactor.region.EW             -17.501  -6.501  0.49862  6.499 19.499
## nodefactor.region.OW             -32.486 -10.486  0.51379 12.514 35.514
## nodematch.race..wa.B              -2.540  -1.540 -0.53985  1.460  3.460
## nodematch.race..wa.H              -6.269  -2.269 -0.26902  2.731  7.731
## nodematch.race..wa.O             -31.880 -10.880  0.11998 12.120 34.120
## 
## 
## Sample statistics cross-correlations:
##                                       edges
## edges                            1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.54752898
## nodefactor.deg.main.deg.pers.0.2 0.26499853
## nodefactor.deg.main.deg.pers.1.0 0.27359221
## nodefactor.deg.main.deg.pers.1.1 0.49736094
## nodefactor.deg.main.deg.pers.1.2 0.52001009
## nodefactor.race..wa.B            0.38623773
## nodefactor.race..wa.H            0.51343474
## nodefactor.region.EW             0.39093037
## nodefactor.region.OW             0.63871675
## nodematch.race..wa.B             0.07033868
## nodematch.race..wa.H             0.16620551
## nodematch.race..wa.O             0.77054936
##                                  nodefactor.deg.main.deg.pers.0.1
## edges                                                  0.54752898
## nodefactor.deg.main.deg.pers.0.1                       1.00000000
## nodefactor.deg.main.deg.pers.0.2                       0.06242871
## nodefactor.deg.main.deg.pers.1.0                       0.06715536
## nodefactor.deg.main.deg.pers.1.1                       0.13738878
## nodefactor.deg.main.deg.pers.1.2                       0.13573073
## nodefactor.race..wa.B                                  0.22040767
## nodefactor.race..wa.H                                  0.26953645
## nodefactor.region.EW                                   0.22044325
## nodefactor.region.OW                                   0.36826942
## nodematch.race..wa.B                                   0.04501968
## nodematch.race..wa.H                                   0.08730881
## nodematch.race..wa.O                                   0.42562044
##                                  nodefactor.deg.main.deg.pers.0.2
## edges                                                  0.26499853
## nodefactor.deg.main.deg.pers.0.1                       0.06242871
## nodefactor.deg.main.deg.pers.0.2                       1.00000000
## nodefactor.deg.main.deg.pers.1.0                       0.04057182
## nodefactor.deg.main.deg.pers.1.1                       0.05911110
## nodefactor.deg.main.deg.pers.1.2                       0.07490689
## nodefactor.race..wa.B                                  0.09578092
## nodefactor.race..wa.H                                  0.12258375
## nodefactor.region.EW                                   0.10089951
## nodefactor.region.OW                                   0.16886059
## nodematch.race..wa.B                                   0.01486575
## nodematch.race..wa.H                                   0.04042143
## nodematch.race..wa.O                                   0.21582452
##                                  nodefactor.deg.main.deg.pers.1.0
## edges                                                  0.27359221
## nodefactor.deg.main.deg.pers.0.1                       0.06715536
## nodefactor.deg.main.deg.pers.0.2                       0.04057182
## nodefactor.deg.main.deg.pers.1.0                       1.00000000
## nodefactor.deg.main.deg.pers.1.1                       0.07058375
## nodefactor.deg.main.deg.pers.1.2                       0.07931307
## nodefactor.race..wa.B                                  0.09868432
## nodefactor.race..wa.H                                  0.16684848
## nodefactor.region.EW                                   0.11322054
## nodefactor.region.OW                                   0.15952037
## nodematch.race..wa.B                                   0.02309522
## nodematch.race..wa.H                                   0.06743486
## nodematch.race..wa.O                                   0.20006550
##                                  nodefactor.deg.main.deg.pers.1.1
## edges                                                  0.49736094
## nodefactor.deg.main.deg.pers.0.1                       0.13738878
## nodefactor.deg.main.deg.pers.0.2                       0.05911110
## nodefactor.deg.main.deg.pers.1.0                       0.07058375
## nodefactor.deg.main.deg.pers.1.1                       1.00000000
## nodefactor.deg.main.deg.pers.1.2                       0.13178515
## nodefactor.race..wa.B                                  0.16762440
## nodefactor.race..wa.H                                  0.26995456
## nodefactor.region.EW                                   0.19070620
## nodefactor.region.OW                                   0.28144549
## nodematch.race..wa.B                                   0.02487680
## nodematch.race..wa.H                                   0.09383300
## nodematch.race..wa.O                                   0.38537290
##                                  nodefactor.deg.main.deg.pers.1.2
## edges                                                  0.52001009
## nodefactor.deg.main.deg.pers.0.1                       0.13573073
## nodefactor.deg.main.deg.pers.0.2                       0.07490689
## nodefactor.deg.main.deg.pers.1.0                       0.07931307
## nodefactor.deg.main.deg.pers.1.1                       0.13178515
## nodefactor.deg.main.deg.pers.1.2                       1.00000000
## nodefactor.race..wa.B                                  0.17558072
## nodefactor.race..wa.H                                  0.31003375
## nodefactor.region.EW                                   0.21361980
## nodefactor.region.OW                                   0.30473851
## nodematch.race..wa.B                                   0.02793614
## nodematch.race..wa.H                                   0.10590871
## nodematch.race..wa.O                                   0.38566749
##                                  nodefactor.race..wa.B
## edges                                     0.3862377318
## nodefactor.deg.main.deg.pers.0.1          0.2204076747
## nodefactor.deg.main.deg.pers.0.2          0.0957809199
## nodefactor.deg.main.deg.pers.1.0          0.0986843166
## nodefactor.deg.main.deg.pers.1.1          0.1676244030
## nodefactor.deg.main.deg.pers.1.2          0.1755807179
## nodefactor.race..wa.B                     1.0000000000
## nodefactor.race..wa.H                     0.1445119702
## nodefactor.region.EW                      0.1052166444
## nodefactor.region.OW                      0.2296831011
## nodematch.race..wa.B                      0.3554556305
## nodematch.race..wa.H                     -0.0002008646
## nodematch.race..wa.O                      0.0001534158
##                                  nodefactor.race..wa.H
## edges                                      0.513434743
## nodefactor.deg.main.deg.pers.0.1           0.269536452
## nodefactor.deg.main.deg.pers.0.2           0.122583746
## nodefactor.deg.main.deg.pers.1.0           0.166848483
## nodefactor.deg.main.deg.pers.1.1           0.269954558
## nodefactor.deg.main.deg.pers.1.2           0.310033753
## nodefactor.race..wa.B                      0.144511970
## nodefactor.race..wa.H                      1.000000000
## nodefactor.region.EW                       0.310654890
## nodefactor.region.OW                       0.317411839
## nodematch.race..wa.B                      -0.003914832
## nodematch.race..wa.H                       0.552472003
## nodematch.race..wa.O                      -0.007784974
##                                  nodefactor.region.EW nodefactor.region.OW
## edges                                    0.3909303685           0.63871675
## nodefactor.deg.main.deg.pers.0.1         0.2204432536           0.36826942
## nodefactor.deg.main.deg.pers.0.2         0.1008995097           0.16886059
## nodefactor.deg.main.deg.pers.1.0         0.1132205442           0.15952037
## nodefactor.deg.main.deg.pers.1.1         0.1907062043           0.28144549
## nodefactor.deg.main.deg.pers.1.2         0.2136197968           0.30473851
## nodefactor.race..wa.B                    0.1052166444           0.22968310
## nodefactor.race..wa.H                    0.3106548898           0.31741184
## nodefactor.region.EW                     1.0000000000           0.12809479
## nodefactor.region.OW                     0.1280947937           1.00000000
## nodematch.race..wa.B                    -0.0004483979           0.03845368
## nodematch.race..wa.H                     0.1326972437           0.10048030
## nodematch.race..wa.O                     0.2555127385           0.50355447
##                                  nodematch.race..wa.B nodematch.race..wa.H
## edges                                    0.0703386817         0.1662055110
## nodefactor.deg.main.deg.pers.0.1         0.0450196817         0.0873088087
## nodefactor.deg.main.deg.pers.0.2         0.0148657498         0.0404214266
## nodefactor.deg.main.deg.pers.1.0         0.0230952152         0.0674348557
## nodefactor.deg.main.deg.pers.1.1         0.0248768026         0.0938330018
## nodefactor.deg.main.deg.pers.1.2         0.0279361356         0.1059087129
## nodefactor.race..wa.B                    0.3554556305        -0.0002008646
## nodefactor.race..wa.H                   -0.0039148316         0.5524720034
## nodefactor.region.EW                    -0.0004483979         0.1326972437
## nodefactor.region.OW                     0.0384536811         0.1004803001
## nodematch.race..wa.B                     1.0000000000        -0.0067612089
## nodematch.race..wa.H                    -0.0067612089         1.0000000000
## nodematch.race..wa.O                     0.0006397890        -0.0035520425
##                                  nodematch.race..wa.O
## edges                                    0.7705493579
## nodefactor.deg.main.deg.pers.0.1         0.4256204360
## nodefactor.deg.main.deg.pers.0.2         0.2158245181
## nodefactor.deg.main.deg.pers.1.0         0.2000654963
## nodefactor.deg.main.deg.pers.1.1         0.3853729035
## nodefactor.deg.main.deg.pers.1.2         0.3856674919
## nodefactor.race..wa.B                    0.0001534158
## nodefactor.race..wa.H                   -0.0077849739
## nodefactor.region.EW                     0.2555127385
## nodefactor.region.OW                     0.5035544662
## nodematch.race..wa.B                     0.0006397890
## nodematch.race..wa.H                    -0.0035520425
## nodematch.race..wa.O                     1.0000000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.006857364                      0.004856506
## Lag 2e+05  0.003910362                      0.003024641
## Lag 3e+05  0.019707107                      0.008965933
## Lag 4e+05  0.023564105                     -0.016258853
## Lag 5e+05 -0.009774674                     -0.005776247
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.001243093
## Lag 2e+05                     -0.025208849
## Lag 3e+05                      0.009095668
## Lag 4e+05                      0.016170421
## Lag 5e+05                     -0.019582054
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.003885390
## Lag 2e+05                      0.032108561
## Lag 3e+05                     -0.011630055
## Lag 4e+05                      0.036018473
## Lag 5e+05                      0.003527715
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.003968618
## Lag 2e+05                      0.008028347
## Lag 3e+05                     -0.026856848
## Lag 4e+05                     -0.003109575
## Lag 5e+05                     -0.037110955
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.020796567           0.004664558
## Lag 2e+05                      0.024630196           0.028857555
## Lag 3e+05                      0.008546112           0.003628222
## Lag 4e+05                      0.040971677          -0.001177519
## Lag 5e+05                      0.010688776          -0.003249886
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05          -0.024390153           0.01816161          0.016278356
## Lag 2e+05           0.004583050           0.01039366          0.013823418
## Lag 3e+05           0.030432924           0.01329248          0.035599604
## Lag 4e+05           0.005322091           0.02424222         -0.005025381
## Lag 5e+05          -0.035730246          -0.01244155          0.003955380
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.015777705         -0.002367388          0.005550079
## Lag 2e+05          0.032489559          0.014289877         -0.003035556
## Lag 3e+05          0.031277237         -0.010899733          0.009592418
## Lag 4e+05         -0.019487498          0.021113497          0.021344585
## Lag 5e+05         -0.008853964         -0.019691787          0.007987119
## Chain 2 
##                 edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.00000000                      1.000000000
## Lag 1e+05  0.01103306                      0.030050914
## Lag 2e+05  0.01100291                     -0.013990114
## Lag 3e+05 -0.01342109                     -0.001859018
## Lag 4e+05 -0.04684690                     -0.025624504
## Lag 5e+05  0.02131710                      0.019840955
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.015124228
## Lag 2e+05                     -0.009721158
## Lag 3e+05                     -0.005915169
## Lag 4e+05                      0.007478852
## Lag 5e+05                      0.007019354
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                           1.00000000
## Lag 1e+05                      -0.01223543
## Lag 2e+05                       0.00563004
## Lag 3e+05                      -0.01670851
## Lag 4e+05                       0.00132646
## Lag 5e+05                      -0.01436945
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                         1.000000e+00
## Lag 1e+05                    -1.438197e-05
## Lag 2e+05                     2.328759e-03
## Lag 3e+05                    -3.178101e-03
## Lag 4e+05                    -1.689793e-02
## Lag 5e+05                     2.015815e-02
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                           1.00000000           1.000000000
## Lag 1e+05                       0.02727398           0.016943241
## Lag 2e+05                       0.01107867          -0.027627352
## Lag 3e+05                      -0.02027872          -0.002972669
## Lag 4e+05                      -0.03987154          -0.001929146
## Lag 5e+05                      -0.01734441          -0.012216336
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05          -0.005849324        -0.0336183662         -0.012222922
## Lag 2e+05           0.012244857        -0.0192988767         -0.006419914
## Lag 3e+05          -0.012706844        -0.0266849443         -0.011409515
## Lag 4e+05          -0.007361600         0.0209558632         -0.033607673
## Lag 5e+05           0.015799440        -0.0005670759          0.025182569
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.008263549         -0.004677559         -0.002261754
## Lag 2e+05         -0.008954718         -0.030364122          0.011965965
## Lag 3e+05          0.020774504         -0.028227253         -0.008709123
## Lag 4e+05          0.010737153          0.004341368         -0.042021758
## Lag 5e+05          0.019375801         -0.002639466          0.015483588
## Chain 3 
##                   edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.0000000000                      1.000000000
## Lag 1e+05 -0.0137887354                     -0.006275565
## Lag 2e+05 -0.0309882766                      0.015693623
## Lag 3e+05  0.0004595795                      0.005969373
## Lag 4e+05  0.0058862466                     -0.007628838
## Lag 5e+05  0.0184126137                      0.007044716
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.021287740
## Lag 2e+05                     -0.017500913
## Lag 3e+05                     -0.004985468
## Lag 4e+05                     -0.012773376
## Lag 5e+05                     -0.002975086
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.003140334
## Lag 2e+05                      0.008167299
## Lag 3e+05                      0.031695904
## Lag 4e+05                      0.007523533
## Lag 5e+05                     -0.006099698
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.034172164
## Lag 2e+05                      0.010750652
## Lag 3e+05                     -0.032653070
## Lag 4e+05                     -0.007238603
## Lag 5e+05                      0.022498197
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.008210394          -0.014679981
## Lag 2e+05                     -0.016933890          -0.038816859
## Lag 3e+05                      0.006111650          -0.009224949
## Lag 4e+05                      0.008504333           0.007392837
## Lag 5e+05                      0.024707273          -0.008992431
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05          -0.013743185          0.004700272        -0.0307547364
## Lag 2e+05           0.021196421          0.019984168        -0.0188764340
## Lag 3e+05          -0.025633209          0.016863097        -0.0066540694
## Lag 4e+05          -0.015041360         -0.018054944         0.0008458691
## Lag 5e+05           0.007608404         -0.012619412         0.0355958740
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000         1.0000000000
## Lag 1e+05         -0.004884774         0.0071005579        -0.0005501781
## Lag 2e+05         -0.012832593        -0.0005447563        -0.0216348828
## Lag 3e+05          0.004068963        -0.0045517133         0.0211320714
## Lag 4e+05          0.004570705        -0.0332450469         0.0102560900
## Lag 5e+05         -0.006564571        -0.0049544197        -0.0008227577
## Chain 4 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                     1.000000e+00
## Lag 1e+05 -0.020464738                    -6.253281e-04
## Lag 2e+05 -0.009362570                    -3.186040e-03
## Lag 3e+05  0.005770767                     6.158326e-05
## Lag 4e+05 -0.009680727                     8.146496e-03
## Lag 5e+05 -0.007748854                    -2.433249e-02
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.012199504
## Lag 2e+05                      0.015552600
## Lag 3e+05                      0.024935582
## Lag 4e+05                     -0.003207807
## Lag 5e+05                     -0.007248129
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.011169611
## Lag 2e+05                     -0.009397993
## Lag 3e+05                     -0.005013279
## Lag 4e+05                      0.020268019
## Lag 5e+05                     -0.001494078
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                      -0.01841948
## Lag 2e+05                      -0.01384538
## Lag 3e+05                       0.02514384
## Lag 4e+05                      -0.01995305
## Lag 5e+05                       0.01498998
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                         1.0000000000           1.000000000
## Lag 1e+05                    -0.0164954527           0.002508785
## Lag 2e+05                    -0.0005459395          -0.026589501
## Lag 3e+05                     0.0289972064          -0.006597155
## Lag 4e+05                     0.0077954276          -0.006253555
## Lag 5e+05                    -0.0235217761           0.017973726
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000           1.00000000          1.000000000
## Lag 1e+05          -0.013831132           0.00263852         -0.016433263
## Lag 2e+05          -0.009871287          -0.02214665          0.001280443
## Lag 3e+05           0.016507360           0.01258085         -0.018541046
## Lag 4e+05          -0.013388414          -0.01955355          0.023308162
## Lag 5e+05          -0.001465236          -0.01065396          0.005177430
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.018130307          0.008606582         -0.018605715
## Lag 2e+05         -0.005300516         -0.012532649          0.001542252
## Lag 3e+05         -0.002766764         -0.019751599          0.019215660
## Lag 4e+05          0.018498135         -0.003186467         -0.019566126
## Lag 5e+05         -0.008964608         -0.013422580          0.014029447
## Chain 5 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                     1.0000000000
## Lag 1e+05 -0.010544522                     0.0004470325
## Lag 2e+05  0.023665393                     0.0178779349
## Lag 3e+05 -0.004250223                    -0.0078896270
## Lag 4e+05 -0.005547761                    -0.0113721532
## Lag 5e+05 -0.029210991                    -0.0220004551
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.011774685
## Lag 2e+05                      0.017981476
## Lag 3e+05                     -0.006461500
## Lag 4e+05                     -0.007100744
## Lag 5e+05                      0.014499355
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.002786609
## Lag 2e+05                      0.025929956
## Lag 3e+05                     -0.003301308
## Lag 4e+05                     -0.019235472
## Lag 5e+05                     -0.012396865
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0021087087
## Lag 2e+05                    -0.0081523743
## Lag 3e+05                    -0.0029766389
## Lag 4e+05                     0.0005692866
## Lag 5e+05                     0.0004550819
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.004664382          -0.004954394
## Lag 2e+05                      0.007622073          -0.014802400
## Lag 3e+05                      0.008512276          -0.004963595
## Lag 4e+05                      0.013274620           0.000377193
## Lag 5e+05                     -0.040767013          -0.008961745
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.020846860          0.016454831         -0.023028462
## Lag 2e+05           0.019246724          0.003531526         -0.005432976
## Lag 3e+05          -0.001683569         -0.006295849          0.016781211
## Lag 4e+05          -0.028576401         -0.001690049         -0.030489143
## Lag 5e+05           0.003302175         -0.013912959         -0.007981099
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000         1.0000000000         1.0000000000
## Lag 1e+05        -0.0190454343        -0.0029653229         0.0136351622
## Lag 2e+05        -0.0213980771         0.0035440512         0.0333809175
## Lag 3e+05         0.0001292385         0.0098815139         0.0043772468
## Lag 4e+05         0.0101747588        -0.0003639628         0.0065746060
## Lag 5e+05        -0.0223436420        -0.0005130279         0.0004111833
## Chain 6 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.022130828                     -0.019921715
## Lag 2e+05  0.003471658                      0.013803928
## Lag 3e+05  0.015851038                      0.026784032
## Lag 4e+05 -0.011203800                      0.010803650
## Lag 5e+05  0.003664508                      0.005460753
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.001164807
## Lag 2e+05                      0.017517501
## Lag 3e+05                     -0.004993148
## Lag 4e+05                     -0.001479688
## Lag 5e+05                      0.012585743
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.023643366
## Lag 2e+05                     -0.023637970
## Lag 3e+05                     -0.006503763
## Lag 4e+05                      0.007789211
## Lag 5e+05                      0.040536898
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.005794186
## Lag 2e+05                      0.008858028
## Lag 3e+05                     -0.018278868
## Lag 4e+05                      0.013501888
## Lag 5e+05                     -0.008546801
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                     -0.005809032           0.003121355
## Lag 2e+05                     -0.001690090          -0.012198407
## Lag 3e+05                      0.014073197           0.045017733
## Lag 4e+05                     -0.041903662           0.012917452
## Lag 5e+05                     -0.002125754           0.041270097
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05           0.011752168          0.021416780          0.031318722
## Lag 2e+05          -0.010429770          0.012227009         -0.015316584
## Lag 3e+05          -0.010077544          0.007280407          0.002831317
## Lag 4e+05          -0.008879258          0.004484113          0.021719733
## Lag 5e+05          -0.001821152          0.014454137          0.008184565
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000         1.0000000000          1.000000000
## Lag 1e+05         -0.024378104        -0.0048389952          0.005085264
## Lag 2e+05         -0.005922276        -0.0053521936          0.012635583
## Lag 3e+05          0.006139092        -0.0009187231         -0.005351015
## Lag 4e+05          0.021050639         0.0087315738         -0.013625583
## Lag 5e+05          0.005171875         0.0129355261          0.001150354
## Chain 7 
##                   edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.0000000000                      1.000000000
## Lag 1e+05  0.0053506718                     -0.008549794
## Lag 2e+05  0.0325961982                     -0.023383586
## Lag 3e+05 -0.0513304168                      0.024653615
## Lag 4e+05  0.0007998579                     -0.018373895
## Lag 5e+05 -0.0119199314                      0.017758533
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0008693172
## Lag 2e+05                     0.0119537519
## Lag 3e+05                     0.0103291430
## Lag 4e+05                     0.0070213953
## Lag 5e+05                     0.0432135536
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.018885117
## Lag 2e+05                     -0.006405783
## Lag 3e+05                     -0.029358998
## Lag 4e+05                     -0.016469087
## Lag 5e+05                     -0.005116210
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.012930175
## Lag 2e+05                     -0.004270402
## Lag 3e+05                      0.024594851
## Lag 4e+05                      0.021650277
## Lag 5e+05                     -0.008130142
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                         1.0000000000           1.000000000
## Lag 1e+05                     0.0214626175          -0.001212405
## Lag 2e+05                     0.0007869568           0.020798041
## Lag 3e+05                     0.0129907964           0.001062777
## Lag 4e+05                    -0.0179731678          -0.018889576
## Lag 5e+05                    -0.0211796076          -0.035001842
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.009997464         -0.016408030         -0.026255077
## Lag 2e+05           0.025232727          0.006041130          0.015755153
## Lag 3e+05          -0.021333690          0.003597474         -0.039519446
## Lag 4e+05           0.017557557          0.009019426         -0.030965197
## Lag 5e+05          -0.022972694         -0.008296280         -0.000610623
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0               1.00000000          1.000000000          1.000000000
## Lag 1e+05          -0.03350547          0.014448058          0.003325548
## Lag 2e+05           0.01188963          0.014613867          0.003619067
## Lag 3e+05          -0.00288365         -0.005028990         -0.037624988
## Lag 4e+05           0.01992802          0.002139552          0.005993464
## Lag 5e+05           0.01143258          0.007414210         -0.004064157
## Chain 8 
##                   edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.0000000000                     1.0000000000
## Lag 1e+05 -0.0192109268                    -0.0006549314
## Lag 2e+05  0.0230679659                     0.0198172762
## Lag 3e+05  0.0328499342                     0.0284685902
## Lag 4e+05 -0.0147950006                     0.0026511771
## Lag 5e+05 -0.0005013109                     0.0044660099
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0033845984
## Lag 2e+05                     0.0064923159
## Lag 3e+05                    -0.0151251577
## Lag 4e+05                    -0.0015935392
## Lag 5e+05                    -0.0004746862
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.013928972
## Lag 2e+05                      0.010156539
## Lag 3e+05                     -0.005966959
## Lag 4e+05                     -0.024452593
## Lag 5e+05                      0.011489783
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.005413147
## Lag 2e+05                      0.031953192
## Lag 3e+05                      0.030720649
## Lag 4e+05                      0.009788863
## Lag 5e+05                      0.011431497
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000          1.000000e+00
## Lag 1e+05                      0.016433710          1.564407e-03
## Lag 2e+05                      0.002202676         -1.377610e-02
## Lag 3e+05                     -0.011635197          7.968677e-05
## Lag 4e+05                      0.019459854         -3.384470e-02
## Lag 5e+05                     -0.043379572          1.961191e-02
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000         1.0000000000
## Lag 1e+05          -0.005394442         -0.007797414        -0.0273184186
## Lag 2e+05           0.013153283          0.020686052         0.0456855922
## Lag 3e+05          -0.002493317          0.004274311        -0.0008313697
## Lag 4e+05           0.008795834         -0.012149636        -0.0201929178
## Lag 5e+05          -0.019167059          0.001168394        -0.0089929778
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000          1.000000000          1.000000000
## Lag 1e+05         0.0020925684          0.004361075         -0.011182219
## Lag 2e+05        -0.0025903434         -0.008417035          0.016871717
## Lag 3e+05        -0.0172229544          0.028282203          0.026828334
## Lag 4e+05         0.0002033027         -0.010111699         -0.009326259
## Lag 5e+05         0.0070885836          0.007134567         -0.005470355
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -1.28400                         -1.98562 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.85300                          0.05557 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.08664                         -0.17973 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         -0.06293                         -0.56069 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          0.77017                         -1.56959 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          0.34292                          1.58370 
##             nodematch.race..wa.O 
##                         -1.00752 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.19914023                       0.04707583 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.39365874                       0.95568564 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.93095484                       0.85736111 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.94981847                       0.57500876 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.44120119                       0.11650971 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.73165651                       0.11326283 
##             nodematch.race..wa.O 
##                       0.31368377 
## Joint P-value (lower = worse):  0.9746578 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           2.1123                           0.6056 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           0.1187                           0.8533 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           1.1805                           2.1192 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           0.1126                           2.5205 
##             nodefactor.region.EW             nodefactor.region.OW 
##                           0.9237                           0.6115 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          -0.6631                           0.9490 
##             nodematch.race..wa.O 
##                           0.8955 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.03466245                       0.54478611 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.90549794                       0.39351329 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.23780048                       0.03407294 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.91035207                       0.01171722 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.35565955                       0.54085412 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.50723524                       0.34261286 
##             nodematch.race..wa.O 
##                       0.37052031 
## Joint P-value (lower = worse):  0.6680909 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           2.0044                           1.9741 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           0.3879                           0.5096 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.7513                           0.6653 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           0.6237                           1.1297 
##             nodefactor.region.EW             nodefactor.region.OW 
##                           0.7249                           1.8094 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          -0.3716                           0.0459 
##             nodematch.race..wa.O 
##                           1.2838 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.04502976                       0.04836632 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.69809975                       0.61035554 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.45248779                       0.50587722 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.53282243                       0.25861101 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.46849120                       0.07039518 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.71021271                       0.96339004 
##             nodematch.race..wa.O 
##                       0.19920006 
## Joint P-value (lower = worse):  0.7044862 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.98502                          0.33767 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          1.24584                          1.12029 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.80087                          1.34148 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          2.74139                         -0.19803 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          2.11492                         -0.48622 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          1.00097                         -0.03204 
##             nodematch.race..wa.O 
##                          0.36911 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                      0.324616473                      0.735611469 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                      0.212821391                      0.262589464 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                      0.423209582                      0.179763139 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                      0.006117953                      0.843022093 
##             nodefactor.region.EW             nodefactor.region.OW 
##                      0.034436809                      0.626808299 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                      0.316843552                      0.974444119 
##             nodematch.race..wa.O 
##                      0.712047173 
## Joint P-value (lower = worse):  0.2587543 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           0.9640                           1.7352 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -0.6423                           1.4519 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           0.7724                          -1.2816 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           2.6847                           1.1356 
##             nodefactor.region.EW             nodefactor.region.OW 
##                           0.9013                           0.5564 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                           0.8261                           0.9520 
##             nodematch.race..wa.O 
##                          -0.6304 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                      0.335055866                      0.082697018 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                      0.520705664                      0.146538037 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                      0.439858252                      0.199978477 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                      0.007260082                      0.256127961 
##             nodefactor.region.EW             nodefactor.region.OW 
##                      0.367405843                      0.577945375 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                      0.408775330                      0.341087260 
##             nodematch.race..wa.O 
##                      0.528444679 
## Joint P-value (lower = worse):  0.618702 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.80761                          0.12787 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          2.60278                         -0.83435 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.36362                          0.22635 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          0.74752                         -0.38715 
##             nodefactor.region.EW             nodefactor.region.OW 
##                         -0.01366                          0.34029 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          0.19344                          0.13808 
##             nodematch.race..wa.O 
##                          0.78401 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                      0.419317830                      0.898251407 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                      0.009247115                      0.404085545 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                      0.716140498                      0.820932119 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                      0.454749677                      0.698641638 
##             nodefactor.region.EW             nodefactor.region.OW 
##                      0.989102710                      0.733636389 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                      0.846614009                      0.890179282 
##             nodematch.race..wa.O 
##                      0.433031625 
## Joint P-value (lower = worse):  0.6633008 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -0.9211                          -0.6865 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -1.8034                          -0.2070 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.4554                          -0.9236 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           0.2111                          -1.8484 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          -1.7003                          -0.5093 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                           0.7175                          -1.4255 
##             nodematch.race..wa.O 
##                           0.2140 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.35699124                       0.49239125 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.07132439                       0.83599887 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.64880409                       0.35568550 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.83281523                       0.06454072 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.08906651                       0.61056544 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.47305419                       0.15400544 
##             nodematch.race..wa.O 
##                       0.83051321 
## Joint P-value (lower = worse):  0.6960165 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        -0.349094                         0.141152 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         0.002975                        -1.176087 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        -1.543301                        -0.039222 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         0.221457                         0.925873 
##             nodefactor.region.EW             nodefactor.region.OW 
##                         1.533879                        -0.492842 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                         1.379798                         1.379718 
##             nodematch.race..wa.O 
##                        -0.515833 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.7270189                        0.8877502 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.9976263                        0.2395600 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.1227577                        0.9687133 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.8247368                        0.3545120 
##             nodefactor.region.EW             nodefactor.region.OW 
##                        0.1250594                        0.6221243 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                        0.1676487                        0.1676735 
##             nodematch.race..wa.O 
##                        0.6059710 
## Joint P-value (lower = worse):  0.7523096 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 6

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                                      Mean     SD Naive SE Time-series SE
## edges                             0.76761 21.644 0.124964       0.126295
## nodefactor.deg.main.deg.pers.0.1  0.55069 14.263 0.082345       0.083735
## nodefactor.deg.main.deg.pers.0.2  0.18373  6.155 0.035534       0.035849
## nodefactor.deg.main.deg.pers.1.0  0.29363  6.290 0.036315       0.036135
## nodefactor.deg.main.deg.pers.1.1 -0.11783 12.357 0.071345       0.071803
## nodefactor.deg.main.deg.pers.1.2  0.04348 12.861 0.074255       0.074425
## nodefactor.race..wa.B             0.06768  8.950 0.051670       0.052025
## nodefactor.race..wa.H             0.48585 13.202 0.076224       0.077635
## nodefactor.region.EW              0.19799  9.588 0.055355       0.055776
## nodefactor.region.OW              0.50676 17.363 0.100246       0.100236
## nodematch.race..wa.B              0.03428  1.599 0.009233       0.009288
## nodematch.race..wa.H              0.06688  3.640 0.021016       0.021318
## nodematch.race..wa.O              0.29794 16.817 0.097093       0.096259
## absdiff.sqrt.age                  0.71008 22.482 0.129803       0.128788
## 
## 2. Quantiles for each variable:
## 
##                                     2.5%     25%      50%    75%  97.5%
## edges                            -41.159 -14.159  0.84138 14.841 43.841
## nodefactor.deg.main.deg.pers.0.1 -26.310  -9.310  0.68996  9.690 29.690
## nodefactor.deg.main.deg.pers.0.2 -11.371  -4.371 -0.37103  4.629 12.629
## nodefactor.deg.main.deg.pers.1.0 -11.033  -4.033 -0.03347  3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -23.538  -8.538 -0.53786  8.462 24.462
## nodefactor.deg.main.deg.pers.1.2 -24.388  -8.388 -0.38812  8.612 25.612
## nodefactor.race..wa.B            -16.591  -5.591 -0.59082  6.409 17.409
## nodefactor.race..wa.H            -25.174  -8.174  0.82608  8.826 26.826
## nodefactor.region.EW             -17.501  -6.501 -0.50138  6.499 19.499
## nodefactor.region.OW             -32.486 -11.486  0.51379 11.514 35.514
## nodematch.race..wa.B              -2.540  -1.540 -0.53985  1.460  3.460
## nodematch.race..wa.H              -6.269  -2.269 -0.26902  2.731  7.731
## nodematch.race..wa.O             -31.880 -10.880  0.11998 11.120 33.120
## absdiff.sqrt.age                 -41.955 -14.564  0.20155 15.693 45.578
## 
## 
## Sample statistics cross-correlations:
##                                       edges
## edges                            1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.54949633
## nodefactor.deg.main.deg.pers.0.2 0.26870330
## nodefactor.deg.main.deg.pers.1.0 0.27720480
## nodefactor.deg.main.deg.pers.1.1 0.48865067
## nodefactor.deg.main.deg.pers.1.2 0.49910161
## nodefactor.race..wa.B            0.38359807
## nodefactor.race..wa.H            0.50837573
## nodefactor.region.EW             0.39823200
## nodefactor.region.OW             0.62722088
## nodematch.race..wa.B             0.07450976
## nodematch.race..wa.H             0.15432047
## nodematch.race..wa.O             0.76900448
## absdiff.sqrt.age                 0.76230908
##                                  nodefactor.deg.main.deg.pers.0.1
## edges                                                  0.54949633
## nodefactor.deg.main.deg.pers.0.1                       1.00000000
## nodefactor.deg.main.deg.pers.0.2                       0.07280577
## nodefactor.deg.main.deg.pers.1.0                       0.07747959
## nodefactor.deg.main.deg.pers.1.1                       0.12926714
## nodefactor.deg.main.deg.pers.1.2                       0.13570190
## nodefactor.race..wa.B                                  0.21236310
## nodefactor.race..wa.H                                  0.26429245
## nodefactor.region.EW                                   0.22256580
## nodefactor.region.OW                                   0.36280130
## nodematch.race..wa.B                                   0.04304162
## nodematch.race..wa.H                                   0.07087935
## nodematch.race..wa.O                                   0.42869286
## absdiff.sqrt.age                                       0.42057694
##                                  nodefactor.deg.main.deg.pers.0.2
## edges                                                  0.26870330
## nodefactor.deg.main.deg.pers.0.1                       0.07280577
## nodefactor.deg.main.deg.pers.0.2                       1.00000000
## nodefactor.deg.main.deg.pers.1.0                       0.02478339
## nodefactor.deg.main.deg.pers.1.1                       0.05847135
## nodefactor.deg.main.deg.pers.1.2                       0.06682737
## nodefactor.race..wa.B                                  0.09787326
## nodefactor.race..wa.H                                  0.12875233
## nodefactor.region.EW                                   0.10002281
## nodefactor.region.OW                                   0.17357545
## nodematch.race..wa.B                                   0.01728442
## nodematch.race..wa.H                                   0.02937076
## nodematch.race..wa.O                                   0.21242386
## absdiff.sqrt.age                                       0.20957172
##                                  nodefactor.deg.main.deg.pers.1.0
## edges                                                  0.27720480
## nodefactor.deg.main.deg.pers.0.1                       0.07747959
## nodefactor.deg.main.deg.pers.0.2                       0.02478339
## nodefactor.deg.main.deg.pers.1.0                       1.00000000
## nodefactor.deg.main.deg.pers.1.1                       0.06936366
## nodefactor.deg.main.deg.pers.1.2                       0.05861509
## nodefactor.race..wa.B                                  0.09407941
## nodefactor.race..wa.H                                  0.15337507
## nodefactor.region.EW                                   0.10632333
## nodefactor.region.OW                                   0.15997076
## nodematch.race..wa.B                                   0.01532991
## nodematch.race..wa.H                                   0.05917566
## nodematch.race..wa.O                                   0.21109428
## absdiff.sqrt.age                                       0.20802986
##                                  nodefactor.deg.main.deg.pers.1.1
## edges                                                  0.48865067
## nodefactor.deg.main.deg.pers.0.1                       0.12926714
## nodefactor.deg.main.deg.pers.0.2                       0.05847135
## nodefactor.deg.main.deg.pers.1.0                       0.06936366
## nodefactor.deg.main.deg.pers.1.1                       1.00000000
## nodefactor.deg.main.deg.pers.1.2                       0.11149985
## nodefactor.race..wa.B                                  0.15627399
## nodefactor.race..wa.H                                  0.26534912
## nodefactor.region.EW                                   0.19387240
## nodefactor.region.OW                                   0.27184236
## nodematch.race..wa.B                                   0.02173968
## nodematch.race..wa.H                                   0.09168910
## nodematch.race..wa.O                                   0.37777830
## absdiff.sqrt.age                                       0.37003475
##                                  nodefactor.deg.main.deg.pers.1.2
## edges                                                  0.49910161
## nodefactor.deg.main.deg.pers.0.1                       0.13570190
## nodefactor.deg.main.deg.pers.0.2                       0.06682737
## nodefactor.deg.main.deg.pers.1.0                       0.05861509
## nodefactor.deg.main.deg.pers.1.1                       0.11149985
## nodefactor.deg.main.deg.pers.1.2                       1.00000000
## nodefactor.race..wa.B                                  0.16516477
## nodefactor.race..wa.H                                  0.30780167
## nodefactor.region.EW                                   0.21869388
## nodefactor.region.OW                                   0.28353425
## nodematch.race..wa.B                                   0.02371209
## nodematch.race..wa.H                                   0.10508352
## nodematch.race..wa.O                                   0.36353772
## absdiff.sqrt.age                                       0.38453726
##                                  nodefactor.race..wa.B
## edges                                      0.383598069
## nodefactor.deg.main.deg.pers.0.1           0.212363099
## nodefactor.deg.main.deg.pers.0.2           0.097873260
## nodefactor.deg.main.deg.pers.1.0           0.094079406
## nodefactor.deg.main.deg.pers.1.1           0.156273991
## nodefactor.deg.main.deg.pers.1.2           0.165164767
## nodefactor.race..wa.B                      1.000000000
## nodefactor.race..wa.H                      0.138560203
## nodefactor.region.EW                       0.103077829
## nodefactor.region.OW                       0.222142230
## nodematch.race..wa.B                       0.353600441
## nodematch.race..wa.H                      -0.008394445
## nodematch.race..wa.O                      -0.005389756
## absdiff.sqrt.age                           0.285350770
##                                  nodefactor.race..wa.H
## edges                                      0.508375735
## nodefactor.deg.main.deg.pers.0.1           0.264292448
## nodefactor.deg.main.deg.pers.0.2           0.128752332
## nodefactor.deg.main.deg.pers.1.0           0.153375068
## nodefactor.deg.main.deg.pers.1.1           0.265349120
## nodefactor.deg.main.deg.pers.1.2           0.307801669
## nodefactor.race..wa.B                      0.138560203
## nodefactor.race..wa.H                      1.000000000
## nodefactor.region.EW                       0.325830941
## nodefactor.region.OW                       0.303640531
## nodematch.race..wa.B                       0.003708876
## nodematch.race..wa.H                       0.545236042
## nodematch.race..wa.O                      -0.012380987
## absdiff.sqrt.age                           0.383963280
##                                  nodefactor.region.EW nodefactor.region.OW
## edges                                      0.39823200           0.62722088
## nodefactor.deg.main.deg.pers.0.1           0.22256580           0.36280130
## nodefactor.deg.main.deg.pers.0.2           0.10002281           0.17357545
## nodefactor.deg.main.deg.pers.1.0           0.10632333           0.15997076
## nodefactor.deg.main.deg.pers.1.1           0.19387240           0.27184236
## nodefactor.deg.main.deg.pers.1.2           0.21869388           0.28353425
## nodefactor.race..wa.B                      0.10307783           0.22214223
## nodefactor.race..wa.H                      0.32583094           0.30364053
## nodefactor.region.EW                       1.00000000           0.12004905
## nodefactor.region.OW                       0.12004905           1.00000000
## nodematch.race..wa.B                       0.01861416           0.04593225
## nodematch.race..wa.H                       0.14152435           0.08737416
## nodematch.race..wa.O                       0.25569434           0.49921057
## absdiff.sqrt.age                           0.29859331           0.48644789
##                                  nodematch.race..wa.B nodematch.race..wa.H
## edges                                     0.074509756          0.154320467
## nodefactor.deg.main.deg.pers.0.1          0.043041618          0.070879348
## nodefactor.deg.main.deg.pers.0.2          0.017284421          0.029370762
## nodefactor.deg.main.deg.pers.1.0          0.015329911          0.059175657
## nodefactor.deg.main.deg.pers.1.1          0.021739675          0.091689095
## nodefactor.deg.main.deg.pers.1.2          0.023712091          0.105083519
## nodefactor.race..wa.B                     0.353600441         -0.008394445
## nodefactor.race..wa.H                     0.003708876          0.545236042
## nodefactor.region.EW                      0.018614162          0.141524351
## nodefactor.region.OW                      0.045932254          0.087374160
## nodematch.race..wa.B                      1.000000000          0.004709589
## nodematch.race..wa.H                      0.004709589          1.000000000
## nodematch.race..wa.O                      0.001568777         -0.009186186
## absdiff.sqrt.age                          0.058868379          0.112325276
##                                  nodematch.race..wa.O absdiff.sqrt.age
## edges                                     0.769004480       0.76230908
## nodefactor.deg.main.deg.pers.0.1          0.428692864       0.42057694
## nodefactor.deg.main.deg.pers.0.2          0.212423855       0.20957172
## nodefactor.deg.main.deg.pers.1.0          0.211094282       0.20802986
## nodefactor.deg.main.deg.pers.1.1          0.377778304       0.37003475
## nodefactor.deg.main.deg.pers.1.2          0.363537715       0.38453726
## nodefactor.race..wa.B                    -0.005389756       0.28535077
## nodefactor.race..wa.H                    -0.012380987       0.38396328
## nodefactor.region.EW                      0.255694338       0.29859331
## nodefactor.region.OW                      0.499210566       0.48644789
## nodematch.race..wa.B                      0.001568777       0.05886838
## nodematch.race..wa.H                     -0.009186186       0.11232528
## nodematch.race..wa.O                      1.000000000       0.59049730
## absdiff.sqrt.age                          0.590497303       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                 edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.00000000                      1.000000000
## Lag 1e+05 -0.01838498                     -0.014758308
## Lag 2e+05  0.01598074                     -0.001516905
## Lag 3e+05 -0.01026604                     -0.013136896
## Lag 4e+05 -0.02123864                      0.007924120
## Lag 5e+05 -0.01050437                      0.019076229
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.010866929
## Lag 2e+05                      0.005635868
## Lag 3e+05                     -0.015559389
## Lag 4e+05                      0.012906926
## Lag 5e+05                     -0.023281395
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.015542960
## Lag 2e+05                      0.008423469
## Lag 3e+05                      0.009015947
## Lag 4e+05                      0.001345310
## Lag 5e+05                      0.028563287
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.029656545
## Lag 2e+05                      0.009392623
## Lag 3e+05                     -0.001069393
## Lag 4e+05                     -0.026948922
## Lag 5e+05                     -0.027750021
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.012147145          -0.005114458
## Lag 2e+05                     -0.004677860          -0.023306932
## Lag 3e+05                     -0.004397924           0.006063398
## Lag 4e+05                     -0.004726936           0.001586781
## Lag 5e+05                      0.004323503           0.026075273
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.012315141         -0.001891927         -0.002783458
## Lag 2e+05           0.009962233         -0.030583596          0.018226933
## Lag 3e+05           0.005961769         -0.023948307         -0.003996027
## Lag 4e+05          -0.001810003          0.010053793         -0.040188301
## Lag 5e+05           0.020095619         -0.015380388         -0.027218348
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000          1.000000000          1.000000000
## Lag 1e+05        -0.0056056102         -0.009078095         -0.023409982
## Lag 2e+05         0.0007188766         -0.003902905          0.017190323
## Lag 3e+05         0.0199507505          0.021899713          0.002128706
## Lag 4e+05        -0.0070741928         -0.024633146         -0.024181541
## Lag 5e+05        -0.0102727114         -0.017095102         -0.023880260
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.044119152
## Lag 2e+05      0.010586364
## Lag 3e+05     -0.003137276
## Lag 4e+05     -0.043296112
## Lag 5e+05      0.001389253
## Chain 2 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.002797316                      0.038988894
## Lag 2e+05 -0.038066245                     -0.017405002
## Lag 3e+05  0.047589817                      0.002488176
## Lag 4e+05  0.017429996                     -0.003775788
## Lag 5e+05 -0.012262448                      0.013459546
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.004983675
## Lag 2e+05                     -0.030674977
## Lag 3e+05                      0.029695282
## Lag 4e+05                     -0.010926361
## Lag 5e+05                      0.005956721
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.034852175
## Lag 2e+05                      0.021842281
## Lag 3e+05                     -0.030134654
## Lag 4e+05                      0.004468475
## Lag 5e+05                     -0.045046513
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                     -0.020530197
## Lag 2e+05                     -0.027099690
## Lag 3e+05                      0.028883278
## Lag 4e+05                      0.003580288
## Lag 5e+05                     -0.008334724
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000          1.0000000000
## Lag 1e+05                     -0.013103530          0.0121560155
## Lag 2e+05                      0.001431149          0.0132639289
## Lag 3e+05                      0.035470918          0.0121829557
## Lag 4e+05                      0.024992887          0.0003554095
## Lag 5e+05                     -0.002261401         -0.0118712153
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.015284727         -0.027435351          0.022614818
## Lag 2e+05          -0.020994257         -0.013873569         -0.016894617
## Lag 3e+05           0.007215828          0.039568373          0.008872646
## Lag 4e+05           0.006690702          0.028559402         -0.003144509
## Lag 5e+05          -0.014126937         -0.002518722         -0.003657729
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000           1.00000000          1.000000000
## Lag 1e+05          0.007407616           0.06438844          0.001810974
## Lag 2e+05         -0.002848102          -0.01988282         -0.049205293
## Lag 3e+05         -0.010013317           0.03849992          0.021323101
## Lag 4e+05          0.013148068           0.01176687          0.003271264
## Lag 5e+05         -0.023046487          -0.01553140         -0.004767803
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05      0.002175808
## Lag 2e+05     -0.028038084
## Lag 3e+05      0.030437855
## Lag 4e+05      0.001604587
## Lag 5e+05      0.015139470
## Chain 3 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                     1.0000000000
## Lag 1e+05 -0.016221742                     0.0071324881
## Lag 2e+05 -0.006747327                     0.0190595001
## Lag 3e+05  0.010805860                    -0.0005895686
## Lag 4e+05 -0.036288916                    -0.0137712801
## Lag 5e+05 -0.001143926                     0.0117173882
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.007669356
## Lag 2e+05                     -0.013881988
## Lag 3e+05                     -0.022510622
## Lag 4e+05                      0.018340721
## Lag 5e+05                     -0.026609009
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.016607362
## Lag 2e+05                      0.009811064
## Lag 3e+05                      0.005721957
## Lag 4e+05                     -0.018348484
## Lag 5e+05                     -0.018174479
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.016967372
## Lag 2e+05                     -0.007939954
## Lag 3e+05                      0.022392378
## Lag 4e+05                     -0.021146710
## Lag 5e+05                     -0.023491414
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000          1.0000000000
## Lag 1e+05                      0.001253585         -0.0204970162
## Lag 2e+05                     -0.014765554          0.0009863994
## Lag 3e+05                     -0.023103602         -0.0049348678
## Lag 4e+05                     -0.002262182         -0.0042127896
## Lag 5e+05                     -0.040906716         -0.0080274482
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05         -0.0187468910         -0.006204450          0.014792826
## Lag 2e+05          0.0318280919          0.012656945         -0.006674452
## Lag 3e+05         -0.0035541951          0.026423320         -0.024725030
## Lag 4e+05         -0.0256128812         -0.019748876         -0.034559350
## Lag 5e+05         -0.0004273554         -0.008824892         -0.010108314
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000           1.00000000          1.000000000
## Lag 1e+05         -0.017573194          -0.01971700          0.006406518
## Lag 2e+05         -0.004895722           0.03893733         -0.015182336
## Lag 3e+05         -0.010218188           0.01169082          0.014071629
## Lag 4e+05         -0.014692188          -0.02141986         -0.037055358
## Lag 5e+05          0.008893737          -0.01138526         -0.021145884
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.013904538
## Lag 2e+05      0.004831224
## Lag 3e+05      0.011585412
## Lag 4e+05     -0.029583173
## Lag 5e+05     -0.011014603
## Chain 4 
##                 edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.00000000                      1.000000000
## Lag 1e+05  0.02944270                      0.039076159
## Lag 2e+05  0.01237700                     -0.007652163
## Lag 3e+05  0.01298179                     -0.010532059
## Lag 4e+05 -0.02835684                     -0.026995775
## Lag 5e+05  0.01490372                     -0.001968773
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.032256470
## Lag 2e+05                      0.025961615
## Lag 3e+05                      0.028824602
## Lag 4e+05                     -0.016019001
## Lag 5e+05                     -0.008991554
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.002384740
## Lag 2e+05                      0.012333401
## Lag 3e+05                     -0.004728158
## Lag 4e+05                      0.001012376
## Lag 5e+05                      0.010337732
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0002776783
## Lag 2e+05                     0.0041557116
## Lag 3e+05                    -0.0023088154
## Lag 4e+05                    -0.0125740408
## Lag 5e+05                     0.0202105115
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                         1.0000000000            1.00000000
## Lag 1e+05                     0.0180114675            0.01300494
## Lag 2e+05                    -0.0264834231            0.01367811
## Lag 3e+05                    -0.0068646337           -0.02203863
## Lag 4e+05                     0.0008971107           -0.02793439
## Lag 5e+05                     0.0176539419           -0.01346020
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05           0.019669866         0.0015491994          0.002777396
## Lag 2e+05           0.018143194        -0.0106291663          0.018364519
## Lag 3e+05           0.002698024         0.0100622655         -0.007571935
## Lag 4e+05          -0.004657459        -0.0071402950         -0.003697450
## Lag 5e+05           0.014956455         0.0004633712          0.003875354
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05          0.018422227          0.006099997          0.009584843
## Lag 2e+05         -0.020916839          0.017683053         -0.004631584
## Lag 3e+05         -0.003998694         -0.015934664          0.017198737
## Lag 4e+05          0.032630478          0.005948582         -0.012980987
## Lag 5e+05         -0.013172548          0.004707689          0.008274248
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05      0.028319897
## Lag 2e+05      0.002121496
## Lag 3e+05      0.003934470
## Lag 4e+05     -0.039136350
## Lag 5e+05      0.007275778
## Chain 5 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05 -0.007085999                      0.002911065
## Lag 2e+05 -0.001770386                     -0.002444332
## Lag 3e+05  0.011976435                     -0.018246576
## Lag 4e+05 -0.014880791                      0.026971282
## Lag 5e+05  0.008472621                      0.008524512
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.006265977
## Lag 2e+05                     -0.020632008
## Lag 3e+05                      0.013111568
## Lag 4e+05                      0.006837112
## Lag 5e+05                      0.005094957
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.008486743
## Lag 2e+05                      0.018258017
## Lag 3e+05                      0.008143710
## Lag 4e+05                      0.006046122
## Lag 5e+05                      0.012240606
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.004383539
## Lag 2e+05                      0.047310856
## Lag 3e+05                     -0.001373634
## Lag 4e+05                      0.001277371
## Lag 5e+05                     -0.003594589
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.021624539           0.027523681
## Lag 2e+05                     -0.013465653          -0.004758868
## Lag 3e+05                      0.007313888          -0.003224983
## Lag 4e+05                      0.021748918          -0.007972807
## Lag 5e+05                     -0.012295394           0.006879417
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000          1.000000000          1.000000000
## Lag 1e+05          -0.026610716         -0.019961770         -0.005881322
## Lag 2e+05          -0.003810682          0.050858615          0.024571134
## Lag 3e+05           0.022186457         -0.002702699          0.007690097
## Lag 4e+05          -0.007414182          0.010086678         -0.012001326
## Lag 5e+05           0.009428566         -0.017933716          0.001602909
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05          0.017439889          0.004833652        -0.0163441685
## Lag 2e+05          0.030550952         -0.011510787        -0.0111230792
## Lag 3e+05         -0.011202169          0.001261522         0.0001902394
## Lag 4e+05         -0.001939769          0.015829435         0.0105400730
## Lag 5e+05         -0.011598238         -0.004461001         0.0056487008
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.009031477
## Lag 2e+05     -0.008959441
## Lag 3e+05     -0.022692882
## Lag 4e+05     -0.029572325
## Lag 5e+05      0.036854862
## Chain 6 
##                  edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.000000000                      1.000000000
## Lag 1e+05  0.006538330                     -0.014553577
## Lag 2e+05  0.007679586                      0.023373584
## Lag 3e+05  0.001931852                     -0.006439550
## Lag 4e+05  0.017374787                     -0.007203221
## Lag 5e+05 -0.001841826                     -0.027269260
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                     -0.009915449
## Lag 2e+05                     -0.005752965
## Lag 3e+05                     -0.014988973
## Lag 4e+05                     -0.005819164
## Lag 5e+05                      0.010308823
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.006679984
## Lag 2e+05                      0.018128687
## Lag 3e+05                     -0.013440242
## Lag 4e+05                      0.014864376
## Lag 5e+05                     -0.021507809
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.038644902
## Lag 2e+05                      0.024109570
## Lag 3e+05                     -0.005750718
## Lag 4e+05                     -0.002202024
## Lag 5e+05                     -0.007177422
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                         1.0000000000          1.0000000000
## Lag 1e+05                    -0.0359055375         -0.0039847583
## Lag 2e+05                    -0.0073360971          0.0012836860
## Lag 3e+05                    -0.0095993528          0.0002155454
## Lag 4e+05                    -0.0122061165         -0.0072916694
## Lag 5e+05                    -0.0009764361          0.0259174612
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0               1.000000000         1.0000000000          1.000000000
## Lag 1e+05           0.033182401         0.0275211992          0.020074815
## Lag 2e+05          -0.012549649        -0.0018563742          0.006354528
## Lag 3e+05          -0.006085256         0.0027181001         -0.017308858
## Lag 4e+05           0.002800106         0.0186912577          0.001022046
## Lag 5e+05           0.005229004        -0.0009137091         -0.009309480
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000          1.000000000
## Lag 1e+05         -0.010104764          0.002196446          0.017252674
## Lag 2e+05          0.007319048         -0.014213306          0.007326767
## Lag 3e+05         -0.004725972          0.008523261          0.009172682
## Lag 4e+05          0.015811489         -0.011237865          0.003531728
## Lag 5e+05         -0.009337976         -0.005538742         -0.003536018
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.017539938
## Lag 2e+05     -0.004069927
## Lag 3e+05     -0.012545034
## Lag 4e+05     -0.001138002
## Lag 5e+05      0.011306997
## Chain 7 
##                   edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.0000000000                      1.000000000
## Lag 1e+05 -0.0009342177                     -0.003295265
## Lag 2e+05  0.0127426469                      0.040990789
## Lag 3e+05  0.0269902732                      0.012936585
## Lag 4e+05  0.0127684808                      0.007218824
## Lag 5e+05 -0.0091356638                     -0.032971143
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0054659436
## Lag 2e+05                     0.0062205825
## Lag 3e+05                     0.0128192101
## Lag 4e+05                     0.0057945877
## Lag 5e+05                     0.0006908509
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.021432275
## Lag 2e+05                      0.004167360
## Lag 3e+05                     -0.010601356
## Lag 4e+05                     -0.000208179
## Lag 5e+05                     -0.046646246
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                          1.000000000
## Lag 1e+05                      0.008611236
## Lag 2e+05                     -0.004873903
## Lag 3e+05                     -0.001276032
## Lag 4e+05                      0.009541293
## Lag 5e+05                     -0.015682131
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000          1.0000000000
## Lag 1e+05                     -0.010416599          0.0337659123
## Lag 2e+05                      0.008254769         -0.0075931293
## Lag 3e+05                      0.017394393         -0.0001458916
## Lag 4e+05                     -0.022338268         -0.0038586366
## Lag 5e+05                     -0.008548088         -0.0205013780
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000          1.000000000
## Lag 1e+05          0.0007526209         -0.004035808          0.000622854
## Lag 2e+05          0.0064666518          0.014809456         -0.007288903
## Lag 3e+05         -0.0132908228          0.003708138         -0.033447724
## Lag 4e+05          0.0306068105          0.011943353          0.003934845
## Lag 5e+05          0.0218326702         -0.007536426         -0.012805164
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0              1.000000000          1.000000000         1.0000000000
## Lag 1e+05          0.001878144          0.008468449        -0.0093726387
## Lag 2e+05          0.009550675          0.001333992         0.0006659832
## Lag 3e+05          0.010640822         -0.036951358         0.0213123517
## Lag 4e+05          0.010491045         -0.006448011         0.0028458846
## Lag 5e+05         -0.002138641         -0.004233080         0.0023277856
##           absdiff.sqrt.age
## Lag 0           1.00000000
## Lag 1e+05      -0.01236563
## Lag 2e+05       0.01647964
## Lag 3e+05       0.01464942
## Lag 4e+05       0.03041538
## Lag 5e+05      -0.01139389
## Chain 8 
##                   edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.0000000000                      1.000000000
## Lag 1e+05 -0.0017686449                      0.008063900
## Lag 2e+05  0.0403405356                      0.007485370
## Lag 3e+05  0.0005160806                     -0.013689582
## Lag 4e+05  0.0082160134                      0.014611285
## Lag 5e+05 -0.0066789900                     -0.001524109
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.016555001
## Lag 2e+05                      0.013099205
## Lag 3e+05                     -0.026657237
## Lag 4e+05                      0.038205957
## Lag 5e+05                      0.001330595
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.030292045
## Lag 2e+05                      0.009607327
## Lag 3e+05                     -0.008053026
## Lag 4e+05                      0.059388537
## Lag 5e+05                     -0.024092744
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0026146777
## Lag 2e+05                    -0.0073107938
## Lag 3e+05                    -0.0040333711
## Lag 4e+05                    -0.0104992134
## Lag 5e+05                    -0.0008759704
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0                          1.000000000           1.000000000
## Lag 1e+05                      0.018565085           0.018864567
## Lag 2e+05                      0.032630889          -0.028497744
## Lag 3e+05                      0.001595481           0.011000142
## Lag 4e+05                      0.014183109          -0.023865864
## Lag 5e+05                      0.030730078           0.005545485
##           nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0              1.0000000000          1.000000000         1.0000000000
## Lag 1e+05         -0.0133581672         -0.030107073         0.0082376103
## Lag 2e+05          0.0006492938         -0.009654135         0.0387046383
## Lag 3e+05          0.0044340161         -0.001866520        -0.0009748118
## Lag 4e+05         -0.0117762565         -0.004907252        -0.0093293641
## Lag 5e+05         -0.0006555530         -0.015473440        -0.0127421300
##           nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0             1.0000000000          1.000000000           1.00000000
## Lag 1e+05        -0.0051308174          0.009047269           0.01718919
## Lag 2e+05        -0.0188945833          0.009964113           0.02564848
## Lag 3e+05         0.0030721403          0.010128556           0.01682155
## Lag 4e+05         0.0006633494         -0.021604822           0.01357745
## Lag 5e+05         0.0092564064          0.007676774          -0.01811159
##           absdiff.sqrt.age
## Lag 0          1.000000000
## Lag 1e+05     -0.026404174
## Lag 2e+05      0.013427761
## Lag 3e+05      0.003649105
## Lag 4e+05      0.007222914
## Lag 5e+05      0.008666724
## 
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -0.5775                          -0.5633 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           0.1446                           0.5731 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                           0.6971                          -1.5456 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          -0.5895                           0.5404 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          -1.8048                          -0.4440 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          -1.2349                           1.0565 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                          -0.4700                          -0.2203 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.56362106                       0.57322397 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.88499260                       0.56654569 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.48576691                       0.12219312 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.55550642                       0.58890510 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.07111079                       0.65702886 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.21688403                       0.29072186 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                       0.63833332                       0.82565892 
## Joint P-value (lower = worse):  0.9642196 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          1.65022                          1.12436 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          1.27883                          0.04935 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          1.77984                          0.11528 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          0.84518                         -0.44186 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          0.76834                          1.15071 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          0.25280                         -1.02093 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                          2.07193                          0.73713 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.09889794                       0.26086195 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.20095748                       0.96064147 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.07510265                       0.90821925 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.39801067                       0.65859143 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.44228531                       0.24985207 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.80042316                       0.30728600 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                       0.03827173                       0.46104217 
## Joint P-value (lower = worse):  0.9485581 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -1.24659                         -1.63185 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.52043                         -0.22741 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -1.87911                          0.37078 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         -0.92635                         -1.12713 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          0.01893                         -0.97085 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                         -1.52098                         -1.10867 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                         -0.38045                         -1.43691 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.21254786                       0.10271116 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.60276484                       0.82010193 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.06022907                       0.71079813 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.35426583                       0.25968835 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.98489550                       0.33162201 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.12826389                       0.26757239 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                       0.70361051                       0.15074343 
## Joint P-value (lower = worse):  0.9631895 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          -0.4686                          -1.5053 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          -0.7964                           1.0538 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.9165                          -1.1164 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           1.2988                          -1.1781 
##             nodefactor.region.EW             nodefactor.region.OW 
##                           0.6065                           0.2100 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                           0.9606                           0.3427 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                           0.1154                           0.4221 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.6393582                        0.1322577 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.4257909                        0.2919848 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.3594114                        0.2642340 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.1940026                        0.2387440 
##             nodefactor.region.EW             nodefactor.region.OW 
##                        0.5441590                        0.8336784 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                        0.3367365                        0.7318272 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                        0.9081239                        0.6729393 
## Joint P-value (lower = worse):  0.7606975 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                           0.4640                           1.7062 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                           0.7765                           0.4734 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          -0.7141                          -0.1294 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                           0.7970                           0.3404 
##             nodefactor.region.EW             nodefactor.region.OW 
##                           1.4964                           0.9669 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                           0.3484                          -0.3286 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                           0.2490                           0.4076 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.64261530                       0.08796631 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.43743215                       0.63589976 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.47514108                       0.89700287 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.42547410                       0.73359247 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.13455832                       0.33360477 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.72754198                       0.74244474 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                       0.80334916                       0.68355687 
## Joint P-value (lower = worse):  0.9233332 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.23728                         -0.72416 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          2.18659                          0.01911 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -1.18780                          1.45040 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                         -0.88590                          0.72793 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          1.09384                          1.34181 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                         -2.08408                          0.33465 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                         -0.19692                          1.45641 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.81243636                       0.46896704 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.02877223                       0.98475651 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.23491297                       0.14694680 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.37566974                       0.46665942 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.27402593                       0.17965873 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.03715314                       0.73789224 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                       0.84389355                       0.14527989 
## Joint P-value (lower = worse):  0.4932672 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.27993                         -0.73026 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.79142                         -0.33090 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.27443                          1.12842 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          0.79965                          0.91546 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          0.26616                          0.07421 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          1.47345                         -1.07903 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                         -0.90519                         -0.49806 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.7795318                        0.4652306 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.4286985                        0.7407165 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.7837510                        0.2591416 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                        0.4239143                        0.3599514 
##             nodefactor.region.EW             nodefactor.region.OW 
##                        0.7901131                        0.9408405 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                        0.1406287                        0.2805728 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                        0.3653633                        0.6184422 
## Joint P-value (lower = worse):  0.7142981 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          1.56787                         -0.15500 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          1.09708                          1.93580 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.15349                          0.01116 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                          1.33038                          0.40114 
##             nodefactor.region.EW             nodefactor.region.OW 
##                          0.40107                          0.72834 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                          1.16342                          1.04032 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                          1.16872                          2.02341 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.11691222                       0.87682385 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.27260850                       0.05289245 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.87801030                       0.99109208 
##            nodefactor.race..wa.B            nodefactor.race..wa.H 
##                       0.18339393                       0.68831443 
##             nodefactor.region.EW             nodefactor.region.OW 
##                       0.68836743                       0.46640404 
##             nodematch.race..wa.B             nodematch.race..wa.H 
##                       0.24466104                       0.29819193 
##             nodematch.race..wa.O                 absdiff.sqrt.age 
##                       0.24251636                       0.04303100 
## Joint P-value (lower = worse):  0.6608029 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 7

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                                        Mean     SD Naive SE Time-series SE
## edges                             0.0136128 21.782 0.125760       0.354999
## nodefactor.deg.main.deg.pers.0.1 -0.0190067 14.279 0.082438       0.280311
## nodefactor.deg.main.deg.pers.0.2 -0.0525007  6.131 0.035400       0.061477
## nodefactor.deg.main.deg.pers.1.0  0.0034290  6.238 0.036013       0.041345
## nodefactor.deg.main.deg.pers.1.1  0.5429360 12.594 0.072713       0.256609
## nodefactor.deg.main.deg.pers.1.2 -0.3842240 12.755 0.073640       0.268522
## nodefactor.riskg.O2              -0.4009250  0.000 0.000000       0.000000
## nodefactor.riskg.O3              -0.4009250  0.000 0.000000       0.000000
## nodefactor.riskg.O4               0.0013825  2.608 0.015059       0.015084
## nodefactor.riskg.Y1              -0.1745304 10.810 0.062411       0.071594
## nodefactor.riskg.Y2               0.0152250  1.170 0.006754       0.006692
## nodefactor.riskg.Y3              -0.0435427  2.861 0.016518       0.016520
## nodefactor.riskg.Y4               0.0055347  8.629 0.049817       0.052549
## nodefactor.race..wa.B             0.1290173  8.967 0.051774       0.157597
## nodefactor.race..wa.H            -0.1319200 13.218 0.076315       0.278496
## nodefactor.region.EW              0.4876244  9.496 0.054827       0.152817
## nodefactor.region.OW             -0.8185392 17.207 0.099344       0.229113
## nodematch.race..wa.B              0.0005486  1.589 0.009177       0.026388
## nodematch.race..wa.H              0.0706131  3.659 0.021125       0.093845
## nodematch.race..wa.O              0.2803769 16.859 0.097335       0.257541
## absdiff.sqrt.age                 -0.1657123 26.564 0.153367       0.191959
## 
## 2. Quantiles for each variable:
## 
##                                      2.5%      25%      50%     75%
## edges                            -42.1586 -15.1586 -0.15862 14.8414
## nodefactor.deg.main.deg.pers.0.1 -27.3100  -9.3100 -0.31004  9.6900
## nodefactor.deg.main.deg.pers.0.2 -11.3710  -4.3710 -0.37103  3.6290
## nodefactor.deg.main.deg.pers.1.0 -12.0335  -4.0335 -0.03347  3.9665
## nodefactor.deg.main.deg.pers.1.1 -23.5379  -8.5379  0.46214  8.4621
## nodefactor.deg.main.deg.pers.1.2 -24.3881  -9.3881 -0.38812  7.6119
## nodefactor.riskg.O2               -0.4009  -0.4009 -0.40092 -0.4009
## nodefactor.riskg.O3               -0.4009  -0.4009 -0.40092 -0.4009
## nodefactor.riskg.O4               -4.8558  -1.8558  0.14418  1.3942
## nodefactor.riskg.Y1              -20.5127  -7.5127 -0.51266  7.4873
## nodefactor.riskg.Y2               -1.3491  -1.3491 -0.34908  0.6509
## nodefactor.riskg.Y3               -5.2024  -2.2024 -0.20238  1.7976
## nodefactor.riskg.Y4              -15.7860  -5.7860  0.21403  5.2140
## nodefactor.race..wa.B            -16.5908  -5.5908  0.40918  6.4092
## nodefactor.race..wa.H            -25.1739  -9.1739 -0.17392  8.8261
## nodefactor.region.EW             -17.5014  -6.5014  0.49862  6.4986
## nodefactor.region.OW             -33.4862 -12.4862 -1.48621 10.5138
## nodematch.race..wa.B              -2.5399  -1.5399 -0.53985  1.4601
## nodematch.race..wa.H              -6.2690  -2.2690 -0.26902  2.7310
## nodematch.race..wa.O             -31.8800 -10.8800  0.11998 11.1200
## absdiff.sqrt.age                 -50.4986 -18.3496 -0.70761 17.3135
##                                    97.5%
## edges                            42.8414
## nodefactor.deg.main.deg.pers.0.1 28.6900
## nodefactor.deg.main.deg.pers.0.2 12.6290
## nodefactor.deg.main.deg.pers.1.0 12.9665
## nodefactor.deg.main.deg.pers.1.1 25.4621
## nodefactor.deg.main.deg.pers.1.2 25.6119
## nodefactor.riskg.O2              -0.4009
## nodefactor.riskg.O3              -0.4009
## nodefactor.riskg.O4               5.1442
## nodefactor.riskg.Y1              21.4873
## nodefactor.riskg.Y2               2.6509
## nodefactor.riskg.Y3               5.7976
## nodefactor.riskg.Y4              17.2140
## nodefactor.race..wa.B            18.4092
## nodefactor.race..wa.H            26.8261
## nodefactor.region.EW             19.4986
## nodefactor.region.OW             33.5138
## nodematch.race..wa.B              3.4601
## nodematch.race..wa.H              7.7310
## nodematch.race..wa.O             34.1200
## absdiff.sqrt.age                 53.5492
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                                       edges
## edges                            1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.55269216
## nodefactor.deg.main.deg.pers.0.2 0.26174052
## nodefactor.deg.main.deg.pers.1.0 0.27712515
## nodefactor.deg.main.deg.pers.1.1 0.50354638
## nodefactor.deg.main.deg.pers.1.2 0.50771115
## nodefactor.riskg.O2                      NA
## nodefactor.riskg.O3                      NA
## nodefactor.riskg.O4              0.12806573
## nodefactor.riskg.Y1              0.46715572
## nodefactor.riskg.Y2              0.04654453
## nodefactor.riskg.Y3              0.13566357
## nodefactor.riskg.Y4              0.37410624
## nodefactor.race..wa.B            0.38977825
## nodefactor.race..wa.H            0.50879071
## nodefactor.region.EW             0.40447032
## nodefactor.region.OW             0.62947557
## nodematch.race..wa.B             0.07365090
## nodematch.race..wa.H             0.16121496
## nodematch.race..wa.O             0.77114338
## absdiff.sqrt.age                 0.66328459
##                                  nodefactor.deg.main.deg.pers.0.1
## edges                                                  0.55269216
## nodefactor.deg.main.deg.pers.0.1                       1.00000000
## nodefactor.deg.main.deg.pers.0.2                       0.07849312
## nodefactor.deg.main.deg.pers.1.0                       0.08195463
## nodefactor.deg.main.deg.pers.1.1                       0.14089799
## nodefactor.deg.main.deg.pers.1.2                       0.13908267
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                            NA
## nodefactor.riskg.O4                                    0.08383399
## nodefactor.riskg.Y1                                    0.28374261
## nodefactor.riskg.Y2                                    0.02038057
## nodefactor.riskg.Y3                                    0.07969436
## nodefactor.riskg.Y4                                    0.19842005
## nodefactor.race..wa.B                                  0.15419755
## nodefactor.race..wa.H                                  0.22210543
## nodefactor.region.EW                                   0.23884984
## nodefactor.region.OW                                   0.37347896
## nodematch.race..wa.B                                   0.02279795
## nodematch.race..wa.H                                   0.04987994
## nodematch.race..wa.O                                   0.48518349
## absdiff.sqrt.age                                       0.37992711
##                                  nodefactor.deg.main.deg.pers.0.2
## edges                                                  0.26174052
## nodefactor.deg.main.deg.pers.0.1                       0.07849312
## nodefactor.deg.main.deg.pers.0.2                       1.00000000
## nodefactor.deg.main.deg.pers.1.0                       0.03756479
## nodefactor.deg.main.deg.pers.1.1                       0.05529445
## nodefactor.deg.main.deg.pers.1.2                       0.06309319
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                            NA
## nodefactor.riskg.O4                                    0.02264006
## nodefactor.riskg.Y1                                    0.11547677
## nodefactor.riskg.Y2                                    0.01972302
## nodefactor.riskg.Y3                                    0.02992664
## nodefactor.riskg.Y4                                    0.08371044
## nodefactor.race..wa.B                                  0.12981358
## nodefactor.race..wa.H                                  0.14518061
## nodefactor.region.EW                                   0.11146497
## nodefactor.region.OW                                   0.17611740
## nodematch.race..wa.B                                   0.01655142
## nodematch.race..wa.H                                   0.05679196
## nodematch.race..wa.O                                   0.18806812
## absdiff.sqrt.age                                       0.16704498
##                                  nodefactor.deg.main.deg.pers.1.0
## edges                                                 0.277125145
## nodefactor.deg.main.deg.pers.0.1                      0.081954633
## nodefactor.deg.main.deg.pers.0.2                      0.037564790
## nodefactor.deg.main.deg.pers.1.0                      1.000000000
## nodefactor.deg.main.deg.pers.1.1                      0.060623339
## nodefactor.deg.main.deg.pers.1.2                      0.075645011
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                            NA
## nodefactor.riskg.O4                                   0.035389247
## nodefactor.riskg.Y1                                   0.122496625
## nodefactor.riskg.Y2                                   0.009880225
## nodefactor.riskg.Y3                                   0.035805591
## nodefactor.riskg.Y4                                   0.091321357
## nodefactor.race..wa.B                                 0.117436055
## nodefactor.race..wa.H                                 0.162080744
## nodefactor.region.EW                                  0.101109682
## nodefactor.region.OW                                  0.158470687
## nodematch.race..wa.B                                  0.018760530
## nodematch.race..wa.H                                  0.055354186
## nodematch.race..wa.O                                  0.199257990
## absdiff.sqrt.age                                      0.180203661
##                                  nodefactor.deg.main.deg.pers.1.1
## edges                                                  0.50354638
## nodefactor.deg.main.deg.pers.0.1                       0.14089799
## nodefactor.deg.main.deg.pers.0.2                       0.05529445
## nodefactor.deg.main.deg.pers.1.0                       0.06062334
## nodefactor.deg.main.deg.pers.1.1                       1.00000000
## nodefactor.deg.main.deg.pers.1.2                       0.12943163
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                            NA
## nodefactor.riskg.O4                                    0.05119761
## nodefactor.riskg.Y1                                    0.21513790
## nodefactor.riskg.Y2                                    0.02038598
## nodefactor.riskg.Y3                                    0.05548881
## nodefactor.riskg.Y4                                    0.18785359
## nodefactor.race..wa.B                                  0.17186241
## nodefactor.race..wa.H                                  0.26729957
## nodefactor.region.EW                                   0.21920779
## nodefactor.region.OW                                   0.32524044
## nodematch.race..wa.B                                   0.02500340
## nodematch.race..wa.H                                   0.08729138
## nodematch.race..wa.O                                   0.38746593
## absdiff.sqrt.age                                       0.32642527
##                                  nodefactor.deg.main.deg.pers.1.2
## edges                                                  0.50771115
## nodefactor.deg.main.deg.pers.0.1                       0.13908267
## nodefactor.deg.main.deg.pers.0.2                       0.06309319
## nodefactor.deg.main.deg.pers.1.0                       0.07564501
## nodefactor.deg.main.deg.pers.1.1                       0.12943163
## nodefactor.deg.main.deg.pers.1.2                       1.00000000
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                            NA
## nodefactor.riskg.O4                                    0.05995723
## nodefactor.riskg.Y1                                    0.25634694
## nodefactor.riskg.Y2                                    0.02976379
## nodefactor.riskg.Y3                                    0.08078148
## nodefactor.riskg.Y4                                    0.21808686
## nodefactor.race..wa.B                                  0.17652196
## nodefactor.race..wa.H                                  0.33790579
## nodefactor.region.EW                                   0.21285425
## nodefactor.region.OW                                   0.29408965
## nodematch.race..wa.B                                   0.01939028
## nodematch.race..wa.H                                   0.12578955
## nodematch.race..wa.O                                   0.35282011
## absdiff.sqrt.age                                       0.34824833
##                                  nodefactor.riskg.O2 nodefactor.riskg.O3
## edges                                             NA                  NA
## nodefactor.deg.main.deg.pers.0.1                  NA                  NA
## nodefactor.deg.main.deg.pers.0.2                  NA                  NA
## nodefactor.deg.main.deg.pers.1.0                  NA                  NA
## nodefactor.deg.main.deg.pers.1.1                  NA                  NA
## nodefactor.deg.main.deg.pers.1.2                  NA                  NA
## nodefactor.riskg.O2                                1                  NA
## nodefactor.riskg.O3                               NA                   1
## nodefactor.riskg.O4                               NA                  NA
## nodefactor.riskg.Y1                               NA                  NA
## nodefactor.riskg.Y2                               NA                  NA
## nodefactor.riskg.Y3                               NA                  NA
## nodefactor.riskg.Y4                               NA                  NA
## nodefactor.race..wa.B                             NA                  NA
## nodefactor.race..wa.H                             NA                  NA
## nodefactor.region.EW                              NA                  NA
## nodefactor.region.OW                              NA                  NA
## nodematch.race..wa.B                              NA                  NA
## nodematch.race..wa.H                              NA                  NA
## nodematch.race..wa.O                              NA                  NA
## absdiff.sqrt.age                                  NA                  NA
##                                  nodefactor.riskg.O4 nodefactor.riskg.Y1
## edges                                    0.128065728          0.46715572
## nodefactor.deg.main.deg.pers.0.1         0.083833992          0.28374261
## nodefactor.deg.main.deg.pers.0.2         0.022640056          0.11547677
## nodefactor.deg.main.deg.pers.1.0         0.035389247          0.12249662
## nodefactor.deg.main.deg.pers.1.1         0.051197615          0.21513790
## nodefactor.deg.main.deg.pers.1.2         0.059957233          0.25634694
## nodefactor.riskg.O2                               NA                  NA
## nodefactor.riskg.O3                               NA                  NA
## nodefactor.riskg.O4                      1.000000000          0.03871932
## nodefactor.riskg.Y1                      0.038719324          1.00000000
## nodefactor.riskg.Y2                      0.007873386          0.00266916
## nodefactor.riskg.Y3                      0.012072310          0.02278592
## nodefactor.riskg.Y4                      0.029387499          0.05418054
## nodefactor.race..wa.B                    0.060141306          0.22540057
## nodefactor.race..wa.H                    0.081299659          0.25828696
## nodefactor.region.EW                     0.054632158          0.19378285
## nodefactor.region.OW                     0.076259678          0.28958441
## nodematch.race..wa.B                     0.007044216          0.05159728
## nodematch.race..wa.H                     0.031002950          0.09477882
## nodematch.race..wa.O                     0.085205807          0.33268661
## absdiff.sqrt.age                         0.062991552          0.65415205
##                                  nodefactor.riskg.Y2 nodefactor.riskg.Y3
## edges                                   0.0465445319         0.135663573
## nodefactor.deg.main.deg.pers.0.1        0.0203805723         0.079694365
## nodefactor.deg.main.deg.pers.0.2        0.0197230196         0.029926636
## nodefactor.deg.main.deg.pers.1.0        0.0098802251         0.035805591
## nodefactor.deg.main.deg.pers.1.1        0.0203859849         0.055488814
## nodefactor.deg.main.deg.pers.1.2        0.0297637900         0.080781475
## nodefactor.riskg.O2                               NA                  NA
## nodefactor.riskg.O3                               NA                  NA
## nodefactor.riskg.O4                     0.0078733859         0.012072310
## nodefactor.riskg.Y1                     0.0026691604         0.022785916
## nodefactor.riskg.Y2                     1.0000000000         0.009692505
## nodefactor.riskg.Y3                     0.0096925049         1.000000000
## nodefactor.riskg.Y4                     0.0005042811         0.020391005
## nodefactor.race..wa.B                   0.0270960675         0.066451184
## nodefactor.race..wa.H                   0.0257554212         0.070953113
## nodefactor.region.EW                    0.0082346753         0.060586693
## nodefactor.region.OW                    0.0310877707         0.089841809
## nodematch.race..wa.B                    0.0087124436         0.024623216
## nodematch.race..wa.H                    0.0093233873         0.023283323
## nodematch.race..wa.O                    0.0310872600         0.099995382
## absdiff.sqrt.age                        0.0699171598         0.186565331
##                                  nodefactor.riskg.Y4 nodefactor.race..wa.B
## edges                                   0.3741062379           0.389778246
## nodefactor.deg.main.deg.pers.0.1        0.1984200470           0.154197555
## nodefactor.deg.main.deg.pers.0.2        0.0837104389           0.129813581
## nodefactor.deg.main.deg.pers.1.0        0.0913213569           0.117436055
## nodefactor.deg.main.deg.pers.1.1        0.1878535851           0.171862408
## nodefactor.deg.main.deg.pers.1.2        0.2180868583           0.176521963
## nodefactor.riskg.O2                               NA                    NA
## nodefactor.riskg.O3                               NA                    NA
## nodefactor.riskg.O4                     0.0293874992           0.060141306
## nodefactor.riskg.Y1                     0.0541805436           0.225400574
## nodefactor.riskg.Y2                     0.0005042811           0.027096067
## nodefactor.riskg.Y3                     0.0203910049           0.066451184
## nodefactor.riskg.Y4                     1.0000000000           0.195347842
## nodefactor.race..wa.B                   0.1953478415           1.000000000
## nodefactor.race..wa.H                   0.2112030663           0.153251606
## nodefactor.region.EW                    0.1729928808           0.100289520
## nodefactor.region.OW                    0.2348012060           0.253972658
## nodematch.race..wa.B                    0.0534265025           0.357320876
## nodematch.race..wa.H                    0.0747140168           0.011459436
## nodematch.race..wa.O                    0.2598511157          -0.001793241
## absdiff.sqrt.age                        0.5367016981           0.312886441
##                                  nodefactor.race..wa.H
## edges                                      0.508790705
## nodefactor.deg.main.deg.pers.0.1           0.222105430
## nodefactor.deg.main.deg.pers.0.2           0.145180608
## nodefactor.deg.main.deg.pers.1.0           0.162080744
## nodefactor.deg.main.deg.pers.1.1           0.267299575
## nodefactor.deg.main.deg.pers.1.2           0.337905788
## nodefactor.riskg.O2                                 NA
## nodefactor.riskg.O3                                 NA
## nodefactor.riskg.O4                        0.081299659
## nodefactor.riskg.Y1                        0.258286957
## nodefactor.riskg.Y2                        0.025755421
## nodefactor.riskg.Y3                        0.070953113
## nodefactor.riskg.Y4                        0.211203066
## nodefactor.race..wa.B                      0.153251606
## nodefactor.race..wa.H                      1.000000000
## nodefactor.region.EW                       0.321712777
## nodefactor.region.OW                       0.288660808
## nodematch.race..wa.B                      -0.001061294
## nodematch.race..wa.H                       0.552081496
## nodematch.race..wa.O                      -0.011658978
## absdiff.sqrt.age                           0.362909784
##                                  nodefactor.region.EW nodefactor.region.OW
## edges                                     0.404470315           0.62947557
## nodefactor.deg.main.deg.pers.0.1          0.238849837           0.37347896
## nodefactor.deg.main.deg.pers.0.2          0.111464974           0.17611740
## nodefactor.deg.main.deg.pers.1.0          0.101109682           0.15847069
## nodefactor.deg.main.deg.pers.1.1          0.219207794           0.32524044
## nodefactor.deg.main.deg.pers.1.2          0.212854247           0.29408965
## nodefactor.riskg.O2                                NA                   NA
## nodefactor.riskg.O3                                NA                   NA
## nodefactor.riskg.O4                       0.054632158           0.07625968
## nodefactor.riskg.Y1                       0.193782849           0.28958441
## nodefactor.riskg.Y2                       0.008234675           0.03108777
## nodefactor.riskg.Y3                       0.060586693           0.08984181
## nodefactor.riskg.Y4                       0.172992881           0.23480121
## nodefactor.race..wa.B                     0.100289520           0.25397266
## nodefactor.race..wa.H                     0.321712777           0.28866081
## nodefactor.region.EW                      1.000000000           0.12740576
## nodefactor.region.OW                      0.127405759           1.00000000
## nodematch.race..wa.B                     -0.002267354           0.05774980
## nodematch.race..wa.H                      0.144149364           0.08665417
## nodematch.race..wa.O                      0.266012263           0.49958119
## absdiff.sqrt.age                          0.272355906           0.41679033
##                                  nodematch.race..wa.B nodematch.race..wa.H
## edges                                     0.073650899          0.161214962
## nodefactor.deg.main.deg.pers.0.1          0.022797950          0.049879941
## nodefactor.deg.main.deg.pers.0.2          0.016551425          0.056791962
## nodefactor.deg.main.deg.pers.1.0          0.018760530          0.055354186
## nodefactor.deg.main.deg.pers.1.1          0.025003396          0.087291377
## nodefactor.deg.main.deg.pers.1.2          0.019390282          0.125789552
## nodefactor.riskg.O2                                NA                   NA
## nodefactor.riskg.O3                                NA                   NA
## nodefactor.riskg.O4                       0.007044216          0.031002950
## nodefactor.riskg.Y1                       0.051597277          0.094778817
## nodefactor.riskg.Y2                       0.008712444          0.009323387
## nodefactor.riskg.Y3                       0.024623216          0.023283323
## nodefactor.riskg.Y4                       0.053426502          0.074714017
## nodefactor.race..wa.B                     0.357320876          0.011459436
## nodefactor.race..wa.H                    -0.001061294          0.552081496
## nodefactor.region.EW                     -0.002267354          0.144149364
## nodefactor.region.OW                      0.057749803          0.086654166
## nodematch.race..wa.B                      1.000000000         -0.010805140
## nodematch.race..wa.H                     -0.010805140          1.000000000
## nodematch.race..wa.O                     -0.001337788         -0.013876379
## absdiff.sqrt.age                          0.071761524          0.130195938
##                                  nodematch.race..wa.O absdiff.sqrt.age
## edges                                     0.771143381       0.66328459
## nodefactor.deg.main.deg.pers.0.1          0.485183494       0.37992711
## nodefactor.deg.main.deg.pers.0.2          0.188068117       0.16704498
## nodefactor.deg.main.deg.pers.1.0          0.199257990       0.18020366
## nodefactor.deg.main.deg.pers.1.1          0.387465930       0.32642527
## nodefactor.deg.main.deg.pers.1.2          0.352820111       0.34824833
## nodefactor.riskg.O2                                NA               NA
## nodefactor.riskg.O3                                NA               NA
## nodefactor.riskg.O4                       0.085205807       0.06299155
## nodefactor.riskg.Y1                       0.332686607       0.65415205
## nodefactor.riskg.Y2                       0.031087260       0.06991716
## nodefactor.riskg.Y3                       0.099995382       0.18656533
## nodefactor.riskg.Y4                       0.259851116       0.53670170
## nodefactor.race..wa.B                    -0.001793241       0.31288644
## nodefactor.race..wa.H                    -0.011658978       0.36290978
## nodefactor.region.EW                      0.266012263       0.27235591
## nodefactor.region.OW                      0.499581190       0.41679033
## nodematch.race..wa.B                     -0.001337788       0.07176152
## nodematch.race..wa.H                     -0.013876379       0.13019594
## nodematch.race..wa.O                      1.000000000       0.47879464
## absdiff.sqrt.age                          0.478794645       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##               edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.0000000                        1.0000000
## Lag 1e+05 0.4617516                        0.5518739
## Lag 2e+05 0.3620683                        0.4463866
## Lag 3e+05 0.2907928                        0.3794946
## Lag 4e+05 0.2575937                        0.3418394
## Lag 5e+05 0.2451094                        0.3234730
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                       0.32300413
## Lag 2e+05                       0.19371759
## Lag 3e+05                       0.12370285
## Lag 4e+05                       0.07046152
## Lag 5e+05                       0.05072761
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.119818787
## Lag 2e+05                      0.025800606
## Lag 3e+05                     -0.019405569
## Lag 4e+05                      0.008195302
## Lag 5e+05                      0.043384663
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                            1.0000000
## Lag 1e+05                        0.6083834
## Lag 2e+05                        0.5286139
## Lag 3e+05                        0.4533106
## Lag 4e+05                        0.4066988
## Lag 5e+05                        0.3715875
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                            1.0000000                 NaN
## Lag 1e+05                        0.5680752                 NaN
## Lag 2e+05                        0.4883576                 NaN
## Lag 3e+05                        0.4338244                 NaN
## Lag 4e+05                        0.3925286                 NaN
## Lag 5e+05                        0.3696824                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0                     NaN         1.000000000          1.00000000
## Lag 1e+05                 NaN         0.003389125          0.09656282
## Lag 2e+05                 NaN         0.003150473          0.04648905
## Lag 3e+05                 NaN         0.006137694         -0.01535688
## Lag 4e+05                 NaN         0.004393475          0.01497092
## Lag 5e+05                 NaN        -0.021636565          0.01874098
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000          1.00000000         1.000000000
## Lag 1e+05         0.001196299         -0.00356037         0.006162418
## Lag 2e+05         0.004789483          0.00547912         0.014343930
## Lag 3e+05        -0.007436475         -0.03529054         0.003418948
## Lag 4e+05         0.039186201         -0.02176417        -0.034488632
## Lag 5e+05        -0.005675322         -0.02284505        -0.006687385
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                 1.0000000             1.0000000            1.0000000
## Lag 1e+05             0.4770707             0.5337883            0.4670780
## Lag 2e+05             0.3812174             0.4391500            0.3675805
## Lag 3e+05             0.3413962             0.3778181            0.2926384
## Lag 4e+05             0.2980567             0.3450032            0.2721962
## Lag 5e+05             0.2757344             0.3046084            0.2258520
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0                1.0000000            1.0000000            1.0000000
## Lag 1e+05            0.4058605            0.3961456            0.5786496
## Lag 2e+05            0.2884763            0.3247512            0.4713281
## Lag 3e+05            0.2088593            0.2575536            0.4108832
## Lag 4e+05            0.1594586            0.2281590            0.3787614
## Lag 5e+05            0.1541566            0.1871063            0.3506148
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0                1.0000000      1.000000000
## Lag 1e+05            0.4436619      0.105842202
## Lag 2e+05            0.3273001      0.058469191
## Lag 3e+05            0.2517783      0.008839262
## Lag 4e+05            0.2095882      0.006503340
## Lag 5e+05            0.1994115      0.041146345
## Chain 2 
##               edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.0000000                        1.0000000
## Lag 1e+05 0.4821474                        0.5744587
## Lag 2e+05 0.3953206                        0.4720723
## Lag 3e+05 0.2988946                        0.4194394
## Lag 4e+05 0.2531850                        0.3781477
## Lag 5e+05 0.2304493                        0.3512590
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                       0.33161973
## Lag 2e+05                       0.21825766
## Lag 3e+05                       0.14205847
## Lag 4e+05                       0.10836140
## Lag 5e+05                       0.06053335
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.114760114
## Lag 2e+05                      0.029990789
## Lag 3e+05                     -0.012820680
## Lag 4e+05                      0.006458531
## Lag 5e+05                      0.021339362
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                            1.0000000
## Lag 1e+05                        0.6216545
## Lag 2e+05                        0.5344574
## Lag 3e+05                        0.4636040
## Lag 4e+05                        0.4278899
## Lag 5e+05                        0.4021712
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                            1.0000000                 NaN
## Lag 1e+05                        0.5481514                 NaN
## Lag 2e+05                        0.4532993                 NaN
## Lag 3e+05                        0.3821849                 NaN
## Lag 4e+05                        0.3631613                 NaN
## Lag 5e+05                        0.3199854                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0                     NaN          1.00000000        1.0000000000
## Lag 1e+05                 NaN          0.02415676        0.0843992636
## Lag 2e+05                 NaN         -0.02512591        0.0567222981
## Lag 3e+05                 NaN         -0.01857925        0.0004666716
## Lag 4e+05                 NaN         -0.01172483        0.0170331598
## Lag 5e+05                 NaN         -0.03144349        0.0077101741
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000        1.0000000000         1.000000000
## Lag 1e+05         0.015094806       -0.0346490548         0.062913495
## Lag 2e+05        -0.018536549       -0.0078877440        -0.007132337
## Lag 3e+05        -0.019096506       -0.0062596451         0.005212813
## Lag 4e+05        -0.011658078       -0.0144534005         0.001409406
## Lag 5e+05         0.002436735        0.0007707817        -0.015950152
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                 1.0000000             1.0000000            1.0000000
## Lag 1e+05             0.4471820             0.5606342            0.4974839
## Lag 2e+05             0.3755829             0.4734767            0.3800522
## Lag 3e+05             0.2999090             0.4015773            0.3092037
## Lag 4e+05             0.2662846             0.3615028            0.2496393
## Lag 5e+05             0.2417790             0.3523887            0.2137153
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0                1.0000000            1.0000000            1.0000000
## Lag 1e+05            0.4171547            0.4442169            0.6322618
## Lag 2e+05            0.3266620            0.3335384            0.5415132
## Lag 3e+05            0.2436514            0.2906234            0.4821461
## Lag 4e+05            0.1825647            0.2453277            0.4470045
## Lag 5e+05            0.1634603            0.2398299            0.4138281
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0                1.0000000       1.00000000
## Lag 1e+05            0.4682185       0.11315524
## Lag 2e+05            0.3573469       0.06601120
## Lag 3e+05            0.2752419       0.01723359
## Lag 4e+05            0.2373397       0.01650095
## Lag 5e+05            0.1933441       0.01344446
## Chain 3 
##               edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.0000000                        1.0000000
## Lag 1e+05 0.5152878                        0.5932103
## Lag 2e+05 0.3933348                        0.4908550
## Lag 3e+05 0.3432345                        0.4331327
## Lag 4e+05 0.3010877                        0.3962899
## Lag 5e+05 0.2659010                        0.3365918
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                       0.32071243
## Lag 2e+05                       0.19649832
## Lag 3e+05                       0.12799678
## Lag 4e+05                       0.06988926
## Lag 5e+05                       0.05086843
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0958434799
## Lag 2e+05                     0.0173925705
## Lag 3e+05                     0.0417055286
## Lag 4e+05                     0.0103444754
## Lag 5e+05                    -0.0004609666
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                            1.0000000
## Lag 1e+05                        0.5803645
## Lag 2e+05                        0.4966492
## Lag 3e+05                        0.4476128
## Lag 4e+05                        0.4059214
## Lag 5e+05                        0.3393920
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                            1.0000000                 NaN
## Lag 1e+05                        0.5704626                 NaN
## Lag 2e+05                        0.4730818                 NaN
## Lag 3e+05                        0.4115392                 NaN
## Lag 4e+05                        0.3684745                 NaN
## Lag 5e+05                        0.3320305                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0                     NaN        1.0000000000          1.00000000
## Lag 1e+05                 NaN       -0.0092246127          0.09770601
## Lag 2e+05                 NaN       -0.0082929602          0.01335332
## Lag 3e+05                 NaN        0.0073303424          0.04091385
## Lag 4e+05                 NaN        0.0001491677          0.05216805
## Lag 5e+05                 NaN        0.0065881474          0.01725712
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05         0.013679155         0.033300272         0.035008652
## Lag 2e+05        -0.009798451         0.002661877         0.027248553
## Lag 3e+05        -0.010883237         0.013315023         0.008474923
## Lag 4e+05        -0.037042868         0.011032127         0.007198264
## Lag 5e+05        -0.001913520         0.025266911         0.019969928
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                 1.0000000             1.0000000            1.0000000
## Lag 1e+05             0.4725789             0.5833735            0.4468567
## Lag 2e+05             0.3541558             0.4936403            0.3226676
## Lag 3e+05             0.3200221             0.4391516            0.2866003
## Lag 4e+05             0.2708024             0.4018216            0.2682385
## Lag 5e+05             0.2341042             0.3528610            0.2263944
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0                1.0000000            1.0000000            1.0000000
## Lag 1e+05            0.4522453            0.4536758            0.6590132
## Lag 2e+05            0.3430464            0.3519906            0.5645806
## Lag 3e+05            0.2898180            0.3068079            0.5047194
## Lag 4e+05            0.2191590            0.2767216            0.4626625
## Lag 5e+05            0.1910342            0.2450838            0.4340865
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0                1.0000000       1.00000000
## Lag 1e+05            0.4935297       0.13390978
## Lag 2e+05            0.3620186       0.02898096
## Lag 3e+05            0.3075598       0.04359864
## Lag 4e+05            0.2676034       0.04337944
## Lag 5e+05            0.2363159       0.03154929
## Chain 4 
##               edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.0000000                        1.0000000
## Lag 1e+05 0.5257671                        0.5821850
## Lag 2e+05 0.4319351                        0.4950363
## Lag 3e+05 0.3875049                        0.4479013
## Lag 4e+05 0.3399653                        0.3944803
## Lag 5e+05 0.3062858                        0.3547651
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                       0.34983430
## Lag 2e+05                       0.22780549
## Lag 3e+05                       0.15408861
## Lag 4e+05                       0.10475110
## Lag 5e+05                       0.07738329
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0968269296
## Lag 2e+05                     0.0453161922
## Lag 3e+05                     0.0100703778
## Lag 4e+05                     0.0016059987
## Lag 5e+05                     0.0004893675
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                            1.0000000
## Lag 1e+05                        0.5814430
## Lag 2e+05                        0.4732159
## Lag 3e+05                        0.4288814
## Lag 4e+05                        0.3972611
## Lag 5e+05                        0.3571744
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                            1.0000000                 NaN
## Lag 1e+05                        0.6089648                 NaN
## Lag 2e+05                        0.5289242                 NaN
## Lag 3e+05                        0.4722774                 NaN
## Lag 4e+05                        0.4284737                 NaN
## Lag 5e+05                        0.4010084                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0                     NaN         1.000000000          1.00000000
## Lag 1e+05                 NaN         0.022842798          0.10955827
## Lag 2e+05                 NaN         0.005854405          0.04471111
## Lag 3e+05                 NaN        -0.005114630          0.01809621
## Lag 4e+05                 NaN        -0.018980118          0.03215190
## Lag 5e+05                 NaN        -0.011898122          0.02537469
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05         0.003681293        -0.026494778         0.030758223
## Lag 2e+05         0.010543653         0.002259701         0.000223715
## Lag 3e+05         0.008308187        -0.004206008         0.010335788
## Lag 4e+05        -0.028469168         0.013053772         0.005120126
## Lag 5e+05         0.004460180        -0.012944679        -0.004098666
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                 1.0000000             1.0000000            1.0000000
## Lag 1e+05             0.4876005             0.5991149            0.5107755
## Lag 2e+05             0.4173559             0.5183276            0.4225394
## Lag 3e+05             0.3641339             0.4684762            0.3711443
## Lag 4e+05             0.3379657             0.4154959            0.3184632
## Lag 5e+05             0.3169650             0.3878736            0.2898231
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0                1.0000000            1.0000000            1.0000000
## Lag 1e+05            0.4342033            0.4536845            0.5921520
## Lag 2e+05            0.3164490            0.3400837            0.4972122
## Lag 3e+05            0.2519794            0.3148857            0.4619920
## Lag 4e+05            0.2259287            0.2717457            0.4038103
## Lag 5e+05            0.2035106            0.2382688            0.3863000
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0                1.0000000      1.000000000
## Lag 1e+05            0.5004109      0.117775544
## Lag 2e+05            0.4081734      0.061832715
## Lag 3e+05            0.3355635      0.056575061
## Lag 4e+05            0.2709065      0.048415038
## Lag 5e+05            0.2572474      0.006347213
## Chain 5 
##               edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.0000000                        1.0000000
## Lag 1e+05 0.5195568                        0.6020423
## Lag 2e+05 0.4255347                        0.5140198
## Lag 3e+05 0.3522717                        0.4604894
## Lag 4e+05 0.3240771                        0.4263072
## Lag 5e+05 0.2692905                        0.3681916
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                       0.32104545
## Lag 2e+05                       0.20384397
## Lag 3e+05                       0.12751360
## Lag 4e+05                       0.07234818
## Lag 5e+05                       0.07516452
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                           1.00000000
## Lag 1e+05                       0.11439768
## Lag 2e+05                       0.01955363
## Lag 3e+05                      -0.01356662
## Lag 4e+05                      -0.02139613
## Lag 5e+05                       0.00427859
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                            1.0000000
## Lag 1e+05                        0.5615289
## Lag 2e+05                        0.4745475
## Lag 3e+05                        0.4109649
## Lag 4e+05                        0.3637328
## Lag 5e+05                        0.3071199
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                            1.0000000                 NaN
## Lag 1e+05                        0.5850843                 NaN
## Lag 2e+05                        0.4965522                 NaN
## Lag 3e+05                        0.4358503                 NaN
## Lag 4e+05                        0.3913332                 NaN
## Lag 5e+05                        0.3566639                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0                     NaN        1.0000000000         1.000000000
## Lag 1e+05                 NaN        0.0100790996         0.106513116
## Lag 2e+05                 NaN        0.0047423633         0.046795500
## Lag 3e+05                 NaN        0.0327110319         0.001557545
## Lag 4e+05                 NaN        0.0101702583         0.008401709
## Lag 5e+05                 NaN       -0.0004345554        -0.002644899
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05        -0.037565294        -0.005040225         0.055244224
## Lag 2e+05         0.009675749         0.008071862         0.038875667
## Lag 3e+05        -0.021627785         0.004790135         0.002192552
## Lag 4e+05         0.002550793         0.010712099         0.021992763
## Lag 5e+05        -0.026619603         0.002025123        -0.010651061
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                 1.0000000             1.0000000            1.0000000
## Lag 1e+05             0.4828654             0.5459904            0.4879740
## Lag 2e+05             0.3926013             0.4463376            0.3971402
## Lag 3e+05             0.3206475             0.3908246            0.3394830
## Lag 4e+05             0.2754259             0.3489053            0.2944079
## Lag 5e+05             0.2506780             0.3054282            0.2576246
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0                1.0000000            1.0000000            1.0000000
## Lag 1e+05            0.4812138            0.4754401            0.6109867
## Lag 2e+05            0.3742156            0.3878946            0.5301779
## Lag 3e+05            0.3084831            0.3172372            0.4783160
## Lag 4e+05            0.2621368            0.2658548            0.4370706
## Lag 5e+05            0.2182334            0.2376339            0.4075848
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0                1.0000000      1.000000000
## Lag 1e+05            0.4974287      0.121265544
## Lag 2e+05            0.4042686      0.091192183
## Lag 3e+05            0.3203541      0.034567793
## Lag 4e+05            0.2908213      0.048582396
## Lag 5e+05            0.2401870      0.008585615
## Chain 6 
##               edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.0000000                        1.0000000
## Lag 1e+05 0.4938925                        0.5860133
## Lag 2e+05 0.3921216                        0.4862437
## Lag 3e+05 0.3375685                        0.4311063
## Lag 4e+05 0.3062491                        0.3959310
## Lag 5e+05 0.2509886                        0.3513956
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                       0.35632564
## Lag 2e+05                       0.23143366
## Lag 3e+05                       0.16554403
## Lag 4e+05                       0.13106224
## Lag 5e+05                       0.08699888
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.113393266
## Lag 2e+05                      0.025868961
## Lag 3e+05                      0.009062043
## Lag 4e+05                      0.017024158
## Lag 5e+05                     -0.010588775
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                            1.0000000
## Lag 1e+05                        0.5674350
## Lag 2e+05                        0.4758616
## Lag 3e+05                        0.4242537
## Lag 4e+05                        0.3754652
## Lag 5e+05                        0.3259764
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                            1.0000000                 NaN
## Lag 1e+05                        0.5437857                 NaN
## Lag 2e+05                        0.4452716                 NaN
## Lag 3e+05                        0.3808797                 NaN
## Lag 4e+05                        0.3485841                 NaN
## Lag 5e+05                        0.3004450                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0                     NaN         1.000000000         1.000000000
## Lag 1e+05                 NaN         0.032759211         0.076443900
## Lag 2e+05                 NaN        -0.027812523         0.006727514
## Lag 3e+05                 NaN        -0.011195242         0.012087149
## Lag 4e+05                 NaN        -0.007368893         0.022673797
## Lag 5e+05                 NaN        -0.021428159         0.030502643
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05         0.016625487        -0.003061917         0.026814203
## Lag 2e+05        -0.008302971        -0.005955496        -0.022912729
## Lag 3e+05        -0.001005777         0.010573527        -0.007063925
## Lag 4e+05         0.011614791        -0.042973787        -0.010485888
## Lag 5e+05         0.006017636         0.029215036        -0.016144326
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                 1.0000000             1.0000000            1.0000000
## Lag 1e+05             0.4788687             0.5717617            0.4659228
## Lag 2e+05             0.3968883             0.4510681            0.3690608
## Lag 3e+05             0.3506905             0.3954648            0.3110669
## Lag 4e+05             0.3314007             0.3357357            0.2850460
## Lag 5e+05             0.2901363             0.2987378            0.2361789
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0                1.0000000            1.0000000            1.0000000
## Lag 1e+05            0.4438687            0.4370132            0.5966878
## Lag 2e+05            0.3307137            0.3585145            0.4855534
## Lag 3e+05            0.2940443            0.3192846            0.4389891
## Lag 4e+05            0.2512835            0.2959774            0.3833441
## Lag 5e+05            0.1919817            0.2482113            0.3423318
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0                1.0000000       1.00000000
## Lag 1e+05            0.4600429       0.11723619
## Lag 2e+05            0.3561076       0.05529574
## Lag 3e+05            0.3054157       0.04026419
## Lag 4e+05            0.2706260       0.03752732
## Lag 5e+05            0.2331903       0.01924481
## Chain 7 
##               edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.0000000                        1.0000000
## Lag 1e+05 0.4673642                        0.5650870
## Lag 2e+05 0.3688911                        0.4658896
## Lag 3e+05 0.2850883                        0.4113738
## Lag 4e+05 0.2538090                        0.3726131
## Lag 5e+05 0.2251903                        0.3410495
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                       0.33635599
## Lag 2e+05                       0.21475565
## Lag 3e+05                       0.14623204
## Lag 4e+05                       0.11516736
## Lag 5e+05                       0.07830631
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                           1.00000000
## Lag 1e+05                       0.08552042
## Lag 2e+05                       0.06781278
## Lag 3e+05                       0.01169976
## Lag 4e+05                       0.04449186
## Lag 5e+05                       0.01523348
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                            1.0000000
## Lag 1e+05                        0.5972407
## Lag 2e+05                        0.5154703
## Lag 3e+05                        0.4556508
## Lag 4e+05                        0.4151433
## Lag 5e+05                        0.3845973
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                            1.0000000                 NaN
## Lag 1e+05                        0.5630397                 NaN
## Lag 2e+05                        0.4657780                 NaN
## Lag 3e+05                        0.4126941                 NaN
## Lag 4e+05                        0.3836983                 NaN
## Lag 5e+05                        0.3664433                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0                     NaN        1.0000000000         1.000000000
## Lag 1e+05                 NaN       -0.0096299025         0.073066812
## Lag 2e+05                 NaN        0.0023708220         0.037745506
## Lag 3e+05                 NaN       -0.0205488936         0.002923169
## Lag 4e+05                 NaN       -0.0089167181         0.017219212
## Lag 5e+05                 NaN       -0.0008050061         0.026482111
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05        -0.023891654         0.007681857         0.073724153
## Lag 2e+05        -0.000545099        -0.023021061         0.016374001
## Lag 3e+05         0.035292348         0.009585206         0.001133503
## Lag 4e+05        -0.018093112        -0.029347813        -0.009096273
## Lag 5e+05        -0.033075013        -0.002017007         0.004870178
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                 1.0000000             1.0000000            1.0000000
## Lag 1e+05             0.4641025             0.5596863            0.4520866
## Lag 2e+05             0.3692539             0.4658478            0.3596169
## Lag 3e+05             0.3100468             0.4082629            0.2899433
## Lag 4e+05             0.2707227             0.3602283            0.2541312
## Lag 5e+05             0.2536538             0.3169068            0.2153863
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0                1.0000000            1.0000000            1.0000000
## Lag 1e+05            0.4087717            0.4321137            0.6532148
## Lag 2e+05            0.2861242            0.3179305            0.5634927
## Lag 3e+05            0.1995025            0.2877844            0.5091594
## Lag 4e+05            0.1755632            0.2568398            0.4849910
## Lag 5e+05            0.1589333            0.2277561            0.4503382
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0                1.0000000       1.00000000
## Lag 1e+05            0.4627009       0.12900456
## Lag 2e+05            0.3612421       0.05784518
## Lag 3e+05            0.2946294       0.01289651
## Lag 4e+05            0.2684787       0.03306910
## Lag 5e+05            0.2233852       0.02882009
## Chain 8 
##               edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.0000000                        1.0000000
## Lag 1e+05 0.4573558                        0.5540059
## Lag 2e+05 0.3595517                        0.4643069
## Lag 3e+05 0.2931380                        0.4013733
## Lag 4e+05 0.2554042                        0.3587489
## Lag 5e+05 0.2180891                        0.3328862
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                       0.33791800
## Lag 2e+05                       0.21299766
## Lag 3e+05                       0.14950376
## Lag 4e+05                       0.08513703
## Lag 5e+05                       0.03928050
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.092500132
## Lag 2e+05                      0.036998542
## Lag 3e+05                      0.001556265
## Lag 4e+05                     -0.001900301
## Lag 5e+05                     -0.015187593
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                            1.0000000
## Lag 1e+05                        0.6110473
## Lag 2e+05                        0.5296708
## Lag 3e+05                        0.4710341
## Lag 4e+05                        0.4455539
## Lag 5e+05                        0.3995931
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0                            1.0000000                 NaN
## Lag 1e+05                        0.5214027                 NaN
## Lag 2e+05                        0.4253006                 NaN
## Lag 3e+05                        0.3542583                 NaN
## Lag 4e+05                        0.3390898                 NaN
## Lag 5e+05                        0.3060891                 NaN
##           nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0                     NaN         1.000000000         1.000000000
## Lag 1e+05                 NaN         0.018767196         0.054304723
## Lag 2e+05                 NaN        -0.019086540         0.041090889
## Lag 3e+05                 NaN        -0.009851984         0.007148319
## Lag 4e+05                 NaN        -0.017022255         0.015901542
## Lag 5e+05                 NaN        -0.033568693         0.015285951
##           nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05         0.001421357         0.007219408         0.029969059
## Lag 2e+05         0.005218810        -0.007846310         0.037026312
## Lag 3e+05        -0.012380537         0.004845966         0.030864363
## Lag 4e+05         0.008230948         0.011931566         0.010252402
## Lag 5e+05         0.011328764         0.002671230         0.003907622
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                 1.0000000             1.0000000            1.0000000
## Lag 1e+05             0.4778621             0.5568974            0.4779257
## Lag 2e+05             0.3880605             0.4674805            0.3649656
## Lag 3e+05             0.3468027             0.3999599            0.3254291
## Lag 4e+05             0.2859974             0.3624096            0.2733304
## Lag 5e+05             0.2580312             0.3449905            0.2372995
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0                1.0000000            1.0000000            1.0000000
## Lag 1e+05            0.4235133            0.3917729            0.6063778
## Lag 2e+05            0.3069669            0.2876847            0.5201691
## Lag 3e+05            0.2545070            0.2606280            0.4631211
## Lag 4e+05            0.2229850            0.2159464            0.4221047
## Lag 5e+05            0.1736785            0.2002641            0.4217910
##           nodematch.race..wa.O absdiff.sqrt.age
## Lag 0                1.0000000       1.00000000
## Lag 1e+05            0.4594764       0.10965892
## Lag 2e+05            0.3656525       0.05465696
## Lag 3e+05            0.3009393       0.05243621
## Lag 4e+05            0.2593768       0.04496274
## Lag 5e+05            0.2177785       0.02678074
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          2.13676                          2.45597 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.75124                          1.75711 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.02213                          0.08728 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                          0.46345                         -1.07877 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          0.45494                          0.52695 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          0.38899                         -0.31912 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          0.44513                          0.95903 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -0.08065                          0.25856 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -1.23046                          1.81103 
##                 absdiff.sqrt.age 
##                          0.22718 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.03261736                       0.01405054 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.45250861                       0.07889948 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.98234500                       0.93044544 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.64304486                       0.28069134 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.64915592                       0.59823058 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.69728072                       0.74963651 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.65622284                       0.33754129 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.93571810                       0.79597411 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.21852599                       0.07013646 
##                 absdiff.sqrt.age 
##                       0.82028555 
## Joint P-value (lower = worse):  0.01878489 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.12930                          0.22174 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          1.19559                          0.64245 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.19670                          0.22072 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                         -0.32865                          0.82481 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                         -0.45832                         -0.53669 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          0.18244                          0.47076 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          3.33869                          2.06597 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          0.07601                         -0.45331 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          1.43058                         -2.55067 
##                 absdiff.sqrt.age 
##                          0.34001 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                     0.8971229652                     0.8245156395 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                     0.2318552352                     0.5205839107 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                     0.8440588640                     0.8253094741 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                     0.7424174044                     0.4094774188 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                     0.6467202956                     0.5914796390 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                     0.8552393217                     0.6378109088 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                     0.0008417479                     0.0388316640 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                     0.9394092693                     0.6503226157 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                     0.1525498272                     0.0107517107 
##                 absdiff.sqrt.age 
##                     0.7338464416 
## Joint P-value (lower = worse):  0.001517838 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          1.35328                         -0.26267 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          0.37895                         -0.88964 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          1.56285                          1.00877 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                          0.24035                          0.52374 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          0.64746                          0.11583 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          0.94382                          1.82123 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          0.06649                         -0.54917 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          0.19058                          0.90956 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -1.84054                          0.46099 
##                 absdiff.sqrt.age 
##                          1.62875 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.17596673                       0.79280815 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.70472201                       0.37365790 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.11808725                       0.31308607 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.81005967                       0.60045861 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.51733533                       0.90779093 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.34526167                       0.06857240 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.94698975                       0.58288658 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.84885223                       0.36305303 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.06568968                       0.64480460 
##                 absdiff.sqrt.age 
##                       0.10336535 
## Joint P-value (lower = worse):  0.1264758 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        -0.990642                        -1.946104 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        -0.084410                         1.662902 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        -0.146287                        -0.654770 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                        -0.048602                         0.706129 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                        -1.544943                        -0.602494 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                        -1.095205                         1.353045 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                        -2.081894                        -0.102797 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                        -0.006502                         1.052185 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                        -1.044548                        -0.908841 
##                 absdiff.sqrt.age 
##                        -0.927505 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.32186061                       0.05164230 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.93273015                       0.09633197 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.88369464                       0.51261590 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.96123639                       0.48010780 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.12236009                       0.54684523 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.27342666                       0.17604123 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.03735214                       0.91812370 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.99481205                       0.29271473 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.29623177                       0.36343401 
##                 absdiff.sqrt.age 
##                       0.35366435 
## Joint P-value (lower = worse):  0.003043613 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       -0.0001959                        0.3589916 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       -2.1822845                        0.2266491 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        1.7056280                       -1.5041554 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       -1.3088271                       -1.3590552 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       -0.1366454                       -1.6819904 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                        0.0858141                        1.0014140 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       -1.4676330                        1.5055534 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                        0.4557819                        3.1260093 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       -1.7082533                        0.5384897 
##                 absdiff.sqrt.age 
##                       -0.5674052 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                      0.999843658                      0.719601383 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                      0.029088541                      0.820696577 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                      0.088077370                      0.132541367 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                      0.190592924                      0.174129103 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                      0.891311128                      0.092570696 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                      0.931614203                      0.316626686 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                      0.142203930                      0.132181868 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                      0.648546814                      0.001771959 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                      0.087589344                      0.590238980 
##                 absdiff.sqrt.age 
##                      0.570438889 
## Joint P-value (lower = worse):  0.1912513 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.18748                          0.20714 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -2.28489                         -1.45104 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -1.62107                          0.48803 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                          0.09541                         -1.18931 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          0.36695                         -0.74309 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                         -1.70663                         -1.44838 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          1.22552                         -0.24523 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -0.92698                          0.52222 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -0.93297                         -0.15354 
##                 absdiff.sqrt.age 
##                         -1.62598 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.85128073                       0.83590137 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.02231900                       0.14676753 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.10500293                       0.62553108 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                       0.92398895                       0.23431725 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                       0.71365413                       0.45743005 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                       0.08789055                       0.14750957 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.22037867                       0.80627546 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.35393454                       0.60151591 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.35083716                       0.87797147 
##                 absdiff.sqrt.age 
##                       0.10395325 
## Joint P-value (lower = worse):  0.004604569 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          1.19266                          1.19752 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.33376                          1.14553 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.23465                          1.38073 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                          2.23418                         -1.04931 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                          0.96255                          0.85054 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                          0.52199                         -0.50498 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          0.51297                          2.67785 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          0.17015                          1.66730 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          0.46735                          0.86519 
##                 absdiff.sqrt.age 
##                         -0.05732 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                      0.233003786                      0.231105001 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                      0.738560684                      0.251989029 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                      0.814478366                      0.167362694 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                      0.025471225                      0.294034998 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                      0.335773293                      0.395023401 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                      0.601674803                      0.613572447 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                      0.607971572                      0.007409687 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                      0.864891659                      0.095454577 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                      0.640246737                      0.386934285 
##                 absdiff.sqrt.age 
##                      0.954289171 
## Joint P-value (lower = worse):  0.9550141 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -3.74003                         -0.80572 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.36613                         -0.83924 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -2.91083                         -1.12220 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                         -0.54124                          0.05853 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                         -0.19901                          1.65029 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                         -2.98968                         -2.45656 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                         -2.43892                         -2.41813 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -0.04881                         -0.89159 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          1.06496                         -1.30163 
##                 absdiff.sqrt.age 
##                         -1.29098 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                     0.0001839947                     0.4204056942 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                     0.7142686365                     0.4013352224 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                     0.0036047146                     0.2617753438 
##              nodefactor.riskg.O2              nodefactor.riskg.O3 
##                              NaN                              NaN 
##              nodefactor.riskg.O4              nodefactor.riskg.Y1 
##                     0.5883391311                     0.9533229246 
##              nodefactor.riskg.Y2              nodefactor.riskg.Y3 
##                     0.8422551796                     0.0988829642 
##              nodefactor.riskg.Y4            nodefactor.race..wa.B 
##                     0.0027927406                     0.0140276284 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                     0.0147313142                     0.0156003457 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                     0.9610722046                     0.3726129809 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                     0.2868945416                     0.1930423411 
##                 absdiff.sqrt.age 
##                     0.1967120796 
## Joint P-value (lower = worse):  0.01161936 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Model 8

## Sample statistics summary:
## 
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05 
## Number of chains = 8 
## Sample size per chain = 3750 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                                      Mean     SD Naive SE Time-series SE
## edges                             4.00168 21.958 0.126775       0.172473
## nodefactor.deg.main.deg.pers.0.1  1.94433 14.421 0.083260       0.136694
## nodefactor.deg.main.deg.pers.0.2  0.61357  6.190 0.035737       0.037537
## nodefactor.deg.main.deg.pers.1.0  0.03950  6.290 0.036316       0.036467
## nodefactor.deg.main.deg.pers.1.1  0.18640 12.376 0.071455       0.115903
## nodefactor.deg.main.deg.pers.1.2  0.52934 12.982 0.074954       0.117498
## nodefactor.riskg.O1              -0.40092  0.000 0.000000       0.000000
## nodefactor.riskg.O2              -0.40092  0.000 0.000000       0.000000
## nodefactor.riskg.O3               0.46142  2.725 0.015734       0.015594
## nodefactor.riskg.O4               0.94690 11.733 0.067743       0.077297
## nodefactor.riskg.Y1               0.04566  1.181 0.006816       0.006888
## nodefactor.riskg.Y2               0.04176  2.882 0.016640       0.016671
## nodefactor.riskg.Y3               0.07850  8.718 0.050334       0.051036
## nodefactor.race..wa.B             3.41338  9.381 0.054163       0.077357
## nodefactor.race..wa.H             0.89948 13.203 0.076228       0.120168
## nodefactor.region.EW              0.54889 11.156 0.064408       0.102806
## nodefactor.region.OW              3.38289 20.502 0.118367       0.153351
## nodematch.race..wa.B              1.72468  2.051 0.011843       0.017389
## nodematch.race..wa.H              0.14125  3.657 0.021111       0.040598
## nodematch.race..wa.O              1.40561 16.855 0.097313       0.127641
## nodematch.region                  3.27894 19.671 0.113571       0.165194
## absdiff.sqrt.age                  3.40263 22.526 0.130055       0.155884
## 
## 2. Quantiles for each variable:
## 
##                                      2.5%      25%      50%     75%
## edges                            -39.1586 -11.1586  3.84138 18.8414
## nodefactor.deg.main.deg.pers.0.1 -25.3100  -8.3100  1.68996 11.6900
## nodefactor.deg.main.deg.pers.0.2 -11.3710  -3.3710  0.62897  4.6290
## nodefactor.deg.main.deg.pers.1.0 -12.0335  -4.0335 -0.03347  3.9665
## nodefactor.deg.main.deg.pers.1.1 -23.5379  -8.5379 -0.53786  8.4621
## nodefactor.deg.main.deg.pers.1.2 -24.3881  -8.3881  0.61188  9.6119
## nodefactor.riskg.O1               -0.4009  -0.4009 -0.40092 -0.4009
## nodefactor.riskg.O2               -0.4009  -0.4009 -0.40092 -0.4009
## nodefactor.riskg.O3               -4.8558  -1.8558  0.14418  2.1442
## nodefactor.riskg.O4              -21.5127  -7.5127  0.48734  8.4873
## nodefactor.riskg.Y1               -1.3491  -0.3491 -0.34908  0.6509
## nodefactor.riskg.Y2               -5.2024  -2.2024 -0.20238  1.7976
## nodefactor.riskg.Y3              -16.7860  -5.7860  0.21403  6.2140
## nodefactor.race..wa.B            -14.5908  -2.5908  3.40918  9.4092
## nodefactor.race..wa.H            -24.1739  -8.1739  0.82608  9.8261
## nodefactor.region.EW             -20.5014  -7.5014  0.49862  7.4986
## nodefactor.region.OW             -35.4862 -10.4862  2.51379 17.5138
## nodematch.race..wa.B              -1.5399   0.4601  1.46015  3.4601
## nodematch.race..wa.H              -6.2690  -2.2690 -0.26902  2.7310
## nodematch.race..wa.O             -30.8800  -9.8800  1.11998 13.1200
## nodematch.region                 -35.3269 -10.3269  2.67310 16.6731
## absdiff.sqrt.age                 -39.8863 -12.0691  3.10443 18.7067
##                                    97.5%
## edges                            46.8414
## nodefactor.deg.main.deg.pers.0.1 30.6900
## nodefactor.deg.main.deg.pers.0.2 13.6290
## nodefactor.deg.main.deg.pers.1.0 12.9665
## nodefactor.deg.main.deg.pers.1.1 25.4621
## nodefactor.deg.main.deg.pers.1.2 26.6119
## nodefactor.riskg.O1              -0.4009
## nodefactor.riskg.O2              -0.4009
## nodefactor.riskg.O3               6.1442
## nodefactor.riskg.O4              24.4873
## nodefactor.riskg.Y1               2.6509
## nodefactor.riskg.Y2               5.7976
## nodefactor.riskg.Y3              17.2140
## nodefactor.race..wa.B            22.4092
## nodefactor.race..wa.H            27.8261
## nodefactor.region.EW             22.4986
## nodefactor.region.OW             44.5138
## nodematch.race..wa.B              6.4601
## nodematch.race..wa.H              7.7310
## nodematch.race..wa.O             35.1200
## nodematch.region                 41.6731
## absdiff.sqrt.age                 48.1163
## 
## 
## Sample statistics cross-correlations:
## Warning in cor(as.matrix(x)): the standard deviation is zero
##                                       edges
## edges                            1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.55075590
## nodefactor.deg.main.deg.pers.0.2 0.27608827
## nodefactor.deg.main.deg.pers.1.0 0.27528881
## nodefactor.deg.main.deg.pers.1.1 0.49329541
## nodefactor.deg.main.deg.pers.1.2 0.51033663
## nodefactor.riskg.O1                      NA
## nodefactor.riskg.O2                      NA
## nodefactor.riskg.O3              0.11782879
## nodefactor.riskg.O4              0.42653991
## nodefactor.riskg.Y1              0.05491746
## nodefactor.riskg.Y2              0.12323439
## nodefactor.riskg.Y3              0.37518185
## nodefactor.race..wa.B            0.38881286
## nodefactor.race..wa.H            0.51553427
## nodefactor.region.EW             0.35048771
## nodefactor.region.OW             0.54655894
## nodematch.race..wa.B             0.09376176
## nodematch.race..wa.H             0.16407190
## nodematch.race..wa.O             0.77489365
## nodematch.region                 0.89452739
## absdiff.sqrt.age                 0.77273964
##                                  nodefactor.deg.main.deg.pers.0.1
## edges                                                  0.55075590
## nodefactor.deg.main.deg.pers.0.1                       1.00000000
## nodefactor.deg.main.deg.pers.0.2                       0.07682811
## nodefactor.deg.main.deg.pers.1.0                       0.06606833
## nodefactor.deg.main.deg.pers.1.1                       0.12864151
## nodefactor.deg.main.deg.pers.1.2                       0.13952608
## nodefactor.riskg.O1                                            NA
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.07390317
## nodefactor.riskg.O4                                    0.26325315
## nodefactor.riskg.Y1                                    0.04226189
## nodefactor.riskg.Y2                                    0.06884458
## nodefactor.riskg.Y3                                    0.21878302
## nodefactor.race..wa.B                                  0.24213344
## nodefactor.race..wa.H                                  0.24199672
## nodefactor.region.EW                                   0.19783614
## nodefactor.region.OW                                   0.36906869
## nodematch.race..wa.B                                   0.06572864
## nodematch.race..wa.H                                   0.06805920
## nodematch.race..wa.O                                   0.44012685
## nodematch.region                                       0.48436129
## absdiff.sqrt.age                                       0.43536615
##                                  nodefactor.deg.main.deg.pers.0.2
## edges                                                  0.27608827
## nodefactor.deg.main.deg.pers.0.1                       0.07682811
## nodefactor.deg.main.deg.pers.0.2                       1.00000000
## nodefactor.deg.main.deg.pers.1.0                       0.03984259
## nodefactor.deg.main.deg.pers.1.1                       0.06280018
## nodefactor.deg.main.deg.pers.1.2                       0.07228193
## nodefactor.riskg.O1                                            NA
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.03334999
## nodefactor.riskg.O4                                    0.12833619
## nodefactor.riskg.Y1                                    0.01026939
## nodefactor.riskg.Y2                                    0.03411059
## nodefactor.riskg.Y3                                    0.09148966
## nodefactor.race..wa.B                                  0.09615413
## nodefactor.race..wa.H                                  0.11801351
## nodefactor.region.EW                                   0.07037202
## nodefactor.region.OW                                   0.14462320
## nodematch.race..wa.B                                   0.01120024
## nodematch.race..wa.H                                   0.03259363
## nodematch.race..wa.O                                   0.23394314
## nodematch.region                                       0.25383212
## absdiff.sqrt.age                                       0.21556928
##                                  nodefactor.deg.main.deg.pers.1.0
## edges                                                 0.275288813
## nodefactor.deg.main.deg.pers.0.1                      0.066068334
## nodefactor.deg.main.deg.pers.0.2                      0.039842593
## nodefactor.deg.main.deg.pers.1.0                      1.000000000
## nodefactor.deg.main.deg.pers.1.1                      0.063681735
## nodefactor.deg.main.deg.pers.1.2                      0.079593459
## nodefactor.riskg.O1                                            NA
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                   0.031804968
## nodefactor.riskg.O4                                   0.120978159
## nodefactor.riskg.Y1                                   0.014080572
## nodefactor.riskg.Y2                                   0.024817267
## nodefactor.riskg.Y3                                   0.104142210
## nodefactor.race..wa.B                                 0.080441570
## nodefactor.race..wa.H                                 0.148436208
## nodefactor.region.EW                                  0.093541007
## nodefactor.region.OW                                  0.123507225
## nodematch.race..wa.B                                  0.007982736
## nodematch.race..wa.H                                  0.052532645
## nodematch.race..wa.O                                  0.220077697
## nodematch.region                                      0.247021964
## absdiff.sqrt.age                                      0.213436497
##                                  nodefactor.deg.main.deg.pers.1.1
## edges                                                  0.49329541
## nodefactor.deg.main.deg.pers.0.1                       0.12864151
## nodefactor.deg.main.deg.pers.0.2                       0.06280018
## nodefactor.deg.main.deg.pers.1.0                       0.06368173
## nodefactor.deg.main.deg.pers.1.1                       1.00000000
## nodefactor.deg.main.deg.pers.1.2                       0.13606934
## nodefactor.riskg.O1                                            NA
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.04180244
## nodefactor.riskg.O4                                    0.20370595
## nodefactor.riskg.Y1                                    0.01104443
## nodefactor.riskg.Y2                                    0.06567203
## nodefactor.riskg.Y3                                    0.17643858
## nodefactor.race..wa.B                                  0.14976788
## nodefactor.race..wa.H                                  0.32674342
## nodefactor.region.EW                                   0.23182729
## nodefactor.region.OW                                   0.18090767
## nodematch.race..wa.B                                   0.02620569
## nodematch.race..wa.H                                   0.13103811
## nodematch.race..wa.O                                   0.35937207
## nodematch.region                                       0.44311805
## absdiff.sqrt.age                                       0.38444712
##                                  nodefactor.deg.main.deg.pers.1.2
## edges                                                  0.51033663
## nodefactor.deg.main.deg.pers.0.1                       0.13952608
## nodefactor.deg.main.deg.pers.0.2                       0.07228193
## nodefactor.deg.main.deg.pers.1.0                       0.07959346
## nodefactor.deg.main.deg.pers.1.1                       0.13606934
## nodefactor.deg.main.deg.pers.1.2                       1.00000000
## nodefactor.riskg.O1                                            NA
## nodefactor.riskg.O2                                            NA
## nodefactor.riskg.O3                                    0.06778756
## nodefactor.riskg.O4                                    0.15987650
## nodefactor.riskg.Y1                                    0.02665306
## nodefactor.riskg.Y2                                    0.06375236
## nodefactor.riskg.Y3                                    0.19918254
## nodefactor.race..wa.B                                  0.14116976
## nodefactor.race..wa.H                                  0.27880833
## nodefactor.region.EW                                   0.15601339
## nodefactor.region.OW                                   0.25405279
## nodematch.race..wa.B                                   0.01949303
## nodematch.race..wa.H                                   0.09905141
## nodematch.race..wa.O                                   0.40961070
## nodematch.region                                       0.46190060
## absdiff.sqrt.age                                       0.39012282
##                                  nodefactor.riskg.O1 nodefactor.riskg.O2
## edges                                             NA                  NA
## nodefactor.deg.main.deg.pers.0.1                  NA                  NA
## nodefactor.deg.main.deg.pers.0.2                  NA                  NA
## nodefactor.deg.main.deg.pers.1.0                  NA                  NA
## nodefactor.deg.main.deg.pers.1.1                  NA                  NA
## nodefactor.deg.main.deg.pers.1.2                  NA                  NA
## nodefactor.riskg.O1                                1                  NA
## nodefactor.riskg.O2                               NA                   1
## nodefactor.riskg.O3                               NA                  NA
## nodefactor.riskg.O4                               NA                  NA
## nodefactor.riskg.Y1                               NA                  NA
## nodefactor.riskg.Y2                               NA                  NA
## nodefactor.riskg.Y3                               NA                  NA
## nodefactor.race..wa.B                             NA                  NA
## nodefactor.race..wa.H                             NA                  NA
## nodefactor.region.EW                              NA                  NA
## nodefactor.region.OW                              NA                  NA
## nodematch.race..wa.B                              NA                  NA
## nodematch.race..wa.H                              NA                  NA
## nodematch.race..wa.O                              NA                  NA
## nodematch.region                                  NA                  NA
## absdiff.sqrt.age                                  NA                  NA
##                                  nodefactor.riskg.O3 nodefactor.riskg.O4
## edges                                   0.1178287947         0.426539913
## nodefactor.deg.main.deg.pers.0.1        0.0739031745         0.263253146
## nodefactor.deg.main.deg.pers.0.2        0.0333499891         0.128336192
## nodefactor.deg.main.deg.pers.1.0        0.0318049681         0.120978159
## nodefactor.deg.main.deg.pers.1.1        0.0418024356         0.203705948
## nodefactor.deg.main.deg.pers.1.2        0.0677875641         0.159876497
## nodefactor.riskg.O1                               NA                  NA
## nodefactor.riskg.O2                               NA                  NA
## nodefactor.riskg.O3                     1.0000000000         0.052219944
## nodefactor.riskg.O4                     0.0522199436         1.000000000
## nodefactor.riskg.Y1                     0.0079808336         0.008051066
## nodefactor.riskg.Y2                     0.0034826690         0.017957512
## nodefactor.riskg.Y3                     0.0122959653         0.070041188
## nodefactor.race..wa.B                   0.0302689339         0.134322876
## nodefactor.race..wa.H                   0.0560046184         0.253434579
## nodefactor.region.EW                    0.0308941542         0.128029743
## nodefactor.region.OW                    0.0690318563         0.224004814
## nodematch.race..wa.B                    0.0006287124         0.020691195
## nodematch.race..wa.H                    0.0114253940         0.092919342
## nodematch.race..wa.O                    0.0990768055         0.323297164
## nodematch.region                        0.1092513209         0.382918530
## absdiff.sqrt.age                        0.1235793938         0.420800666
##                                  nodefactor.riskg.Y1 nodefactor.riskg.Y2
## edges                                    0.054917456         0.123234387
## nodefactor.deg.main.deg.pers.0.1         0.042261889         0.068844581
## nodefactor.deg.main.deg.pers.0.2         0.010269387         0.034110593
## nodefactor.deg.main.deg.pers.1.0         0.014080572         0.024817267
## nodefactor.deg.main.deg.pers.1.1         0.011044431         0.065672034
## nodefactor.deg.main.deg.pers.1.2         0.026653062         0.063752364
## nodefactor.riskg.O1                               NA                  NA
## nodefactor.riskg.O2                               NA                  NA
## nodefactor.riskg.O3                      0.007980834         0.003482669
## nodefactor.riskg.O4                      0.008051066         0.017957512
## nodefactor.riskg.Y1                      1.000000000         0.004055536
## nodefactor.riskg.Y2                      0.004055536         1.000000000
## nodefactor.riskg.Y3                      0.003248239         0.020609561
## nodefactor.race..wa.B                    0.009005634         0.040437021
## nodefactor.race..wa.H                    0.019183802         0.068615233
## nodefactor.region.EW                     0.011028986         0.031903756
## nodefactor.region.OW                     0.025195328         0.063828978
## nodematch.race..wa.B                     0.007217855         0.006160376
## nodematch.race..wa.H                     0.006297866         0.027428254
## nodematch.race..wa.O                     0.052897636         0.094722709
## nodematch.region                         0.049048729         0.113387118
## absdiff.sqrt.age                         0.037454893         0.089467573
##                                  nodefactor.riskg.Y3 nodefactor.race..wa.B
## edges                                    0.375181855           0.388812861
## nodefactor.deg.main.deg.pers.0.1         0.218783024           0.242133441
## nodefactor.deg.main.deg.pers.0.2         0.091489665           0.096154131
## nodefactor.deg.main.deg.pers.1.0         0.104142210           0.080441570
## nodefactor.deg.main.deg.pers.1.1         0.176438578           0.149767879
## nodefactor.deg.main.deg.pers.1.2         0.199182536           0.141169762
## nodefactor.riskg.O1                               NA                    NA
## nodefactor.riskg.O2                               NA                    NA
## nodefactor.riskg.O3                      0.012295965           0.030268934
## nodefactor.riskg.O4                      0.070041188           0.134322876
## nodefactor.riskg.Y1                      0.003248239           0.009005634
## nodefactor.riskg.Y2                      0.020609561           0.040437021
## nodefactor.riskg.Y3                      1.000000000           0.129640514
## nodefactor.race..wa.B                    0.129640514           1.000000000
## nodefactor.race..wa.H                    0.178950837           0.122157151
## nodefactor.region.EW                     0.125226500           0.082151599
## nodefactor.region.OW                     0.215387217           0.217848750
## nodematch.race..wa.B                     0.030706343           0.440985921
## nodematch.race..wa.H                     0.051013278          -0.020769026
## nodematch.race..wa.O                     0.306448339           0.009635829
## nodematch.region                         0.341846962           0.348633856
## absdiff.sqrt.age                         0.277793807           0.299574260
##                                  nodefactor.race..wa.H
## edges                                      0.515534266
## nodefactor.deg.main.deg.pers.0.1           0.241996718
## nodefactor.deg.main.deg.pers.0.2           0.118013511
## nodefactor.deg.main.deg.pers.1.0           0.148436208
## nodefactor.deg.main.deg.pers.1.1           0.326743421
## nodefactor.deg.main.deg.pers.1.2           0.278808328
## nodefactor.riskg.O1                                 NA
## nodefactor.riskg.O2                                 NA
## nodefactor.riskg.O3                        0.056004618
## nodefactor.riskg.O4                        0.253434579
## nodefactor.riskg.Y1                        0.019183802
## nodefactor.riskg.Y2                        0.068615233
## nodefactor.riskg.Y3                        0.178950837
## nodefactor.race..wa.B                      0.122157151
## nodefactor.race..wa.H                      1.000000000
## nodefactor.region.EW                       0.278388507
## nodefactor.region.OW                       0.304500797
## nodematch.race..wa.B                      -0.003224104
## nodematch.race..wa.H                       0.546687141
## nodematch.race..wa.O                       0.010098022
## nodematch.region                           0.444571251
## absdiff.sqrt.age                           0.402106984
##                                  nodefactor.region.EW nodefactor.region.OW
## edges                                      0.35048771           0.54655894
## nodefactor.deg.main.deg.pers.0.1           0.19783614           0.36906869
## nodefactor.deg.main.deg.pers.0.2           0.07037202           0.14462320
## nodefactor.deg.main.deg.pers.1.0           0.09354101           0.12350723
## nodefactor.deg.main.deg.pers.1.1           0.23182729           0.18090767
## nodefactor.deg.main.deg.pers.1.2           0.15601339           0.25405279
## nodefactor.riskg.O1                                NA                   NA
## nodefactor.riskg.O2                                NA                   NA
## nodefactor.riskg.O3                        0.03089415           0.06903186
## nodefactor.riskg.O4                        0.12802974           0.22400481
## nodefactor.riskg.Y1                        0.01102899           0.02519533
## nodefactor.riskg.Y2                        0.03190376           0.06382898
## nodefactor.riskg.Y3                        0.12522650           0.21538722
## nodefactor.race..wa.B                      0.08215160           0.21784875
## nodefactor.race..wa.H                      0.27838851           0.30450080
## nodefactor.region.EW                       1.00000000           0.07245029
## nodefactor.region.OW                       0.07245029           1.00000000
## nodematch.race..wa.B                       0.01360395           0.05382906
## nodematch.race..wa.H                       0.13007125           0.09569426
## nodematch.race..wa.O                       0.23846890           0.40788916
## nodematch.region                           0.20439483           0.43987585
## absdiff.sqrt.age                           0.26411066           0.41498479
##                                  nodematch.race..wa.B nodematch.race..wa.H
## edges                                    0.0937617580          0.164071903
## nodefactor.deg.main.deg.pers.0.1         0.0657286381          0.068059203
## nodefactor.deg.main.deg.pers.0.2         0.0112002445          0.032593633
## nodefactor.deg.main.deg.pers.1.0         0.0079827362          0.052532645
## nodefactor.deg.main.deg.pers.1.1         0.0262056907          0.131038113
## nodefactor.deg.main.deg.pers.1.2         0.0194930349          0.099051405
## nodefactor.riskg.O1                                NA                   NA
## nodefactor.riskg.O2                                NA                   NA
## nodefactor.riskg.O3                      0.0006287124          0.011425394
## nodefactor.riskg.O4                      0.0206911950          0.092919342
## nodefactor.riskg.Y1                      0.0072178551          0.006297866
## nodefactor.riskg.Y2                      0.0061603757          0.027428254
## nodefactor.riskg.Y3                      0.0307063425          0.051013278
## nodefactor.race..wa.B                    0.4409859210         -0.020769026
## nodefactor.race..wa.H                   -0.0032241038          0.546687141
## nodefactor.region.EW                     0.0136039464          0.130071251
## nodefactor.region.OW                     0.0538290553          0.095694259
## nodematch.race..wa.B                     1.0000000000         -0.004350296
## nodematch.race..wa.H                    -0.0043502959          1.000000000
## nodematch.race..wa.O                     0.0028568926          0.010547587
## nodematch.region                         0.0868624510          0.139435840
## absdiff.sqrt.age                         0.0782004881          0.128405685
##                                  nodematch.race..wa.O nodematch.region
## edges                                     0.774893649       0.89452739
## nodefactor.deg.main.deg.pers.0.1          0.440126851       0.48436129
## nodefactor.deg.main.deg.pers.0.2          0.233943139       0.25383212
## nodefactor.deg.main.deg.pers.1.0          0.220077697       0.24702196
## nodefactor.deg.main.deg.pers.1.1          0.359372075       0.44311805
## nodefactor.deg.main.deg.pers.1.2          0.409610702       0.46190060
## nodefactor.riskg.O1                                NA               NA
## nodefactor.riskg.O2                                NA               NA
## nodefactor.riskg.O3                       0.099076805       0.10925132
## nodefactor.riskg.O4                       0.323297164       0.38291853
## nodefactor.riskg.Y1                       0.052897636       0.04904873
## nodefactor.riskg.Y2                       0.094722709       0.11338712
## nodefactor.riskg.Y3                       0.306448339       0.34184696
## nodefactor.race..wa.B                     0.009635829       0.34863386
## nodefactor.race..wa.H                     0.010098022       0.44457125
## nodefactor.region.EW                      0.238468899       0.20439483
## nodefactor.region.OW                      0.407889157       0.43987585
## nodematch.race..wa.B                      0.002856893       0.08686245
## nodematch.race..wa.H                      0.010547587       0.13943584
## nodematch.race..wa.O                      1.000000000       0.70297329
## nodematch.region                          0.702973293       1.00000000
## absdiff.sqrt.age                          0.597908133       0.69272931
##                                  absdiff.sqrt.age
## edges                                  0.77273964
## nodefactor.deg.main.deg.pers.0.1       0.43536615
## nodefactor.deg.main.deg.pers.0.2       0.21556928
## nodefactor.deg.main.deg.pers.1.0       0.21343650
## nodefactor.deg.main.deg.pers.1.1       0.38444712
## nodefactor.deg.main.deg.pers.1.2       0.39012282
## nodefactor.riskg.O1                            NA
## nodefactor.riskg.O2                            NA
## nodefactor.riskg.O3                    0.12357939
## nodefactor.riskg.O4                    0.42080067
## nodefactor.riskg.Y1                    0.03745489
## nodefactor.riskg.Y2                    0.08946757
## nodefactor.riskg.Y3                    0.27779381
## nodefactor.race..wa.B                  0.29957426
## nodefactor.race..wa.H                  0.40210698
## nodefactor.region.EW                   0.26411066
## nodefactor.region.OW                   0.41498479
## nodematch.race..wa.B                   0.07820049
## nodematch.race..wa.H                   0.12840568
## nodematch.race..wa.O                   0.59790813
## nodematch.region                       0.69272931
## absdiff.sqrt.age                       1.00000000
## 
## Sample statistics auto-correlation:
## Chain 1 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.20129993                       0.31491845
## Lag 2e+05 0.09192211                       0.18248211
## Lag 3e+05 0.07455162                       0.10941162
## Lag 4e+05 0.04433054                       0.06415864
## Lag 5e+05 0.03810776                       0.05340657
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.044010539
## Lag 2e+05                     -0.006381757
## Lag 3e+05                      0.004965846
## Lag 4e+05                     -0.012823802
## Lag 5e+05                      0.012648649
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.031346517
## Lag 2e+05                     -0.003480235
## Lag 3e+05                      0.014006528
## Lag 4e+05                      0.003096910
## Lag 5e+05                     -0.018431106
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.33665437
## Lag 2e+05                       0.18166916
## Lag 3e+05                       0.13860459
## Lag 4e+05                       0.10485688
## Lag 5e+05                       0.06166205
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.30003707                 NaN
## Lag 2e+05                       0.17021833                 NaN
## Lag 3e+05                       0.12486357                 NaN
## Lag 4e+05                       0.05685996                 NaN
## Lag 5e+05                       0.05523928                 NaN
##           nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0                     NaN         1.000000000          1.00000000
## Lag 1e+05                 NaN        -0.012079595          0.10855129
## Lag 2e+05                 NaN         0.019493246          0.03132515
## Lag 3e+05                 NaN        -0.009533403          0.05456121
## Lag 4e+05                 NaN        -0.014404772          0.02243770
## Lag 5e+05                 NaN        -0.025252217          0.02664785
##           nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0             1.000000000        1.0000000000         1.000000000
## Lag 1e+05        -0.007813566       -0.0044620383        -0.020195977
## Lag 2e+05        -0.019929844        0.0438982115        -0.004298346
## Lag 3e+05         0.020459452        0.0009948979        -0.012219839
## Lag 4e+05         0.035704660       -0.0307580042         0.007501289
## Lag 5e+05         0.033660902       -0.0324136717         0.013721945
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.20994847            0.28506057           0.26779707
## Lag 2e+05            0.09485882            0.16483074           0.15729315
## Lag 3e+05            0.07772897            0.12237209           0.10489483
## Lag 4e+05            0.06123782            0.07289491           0.06876030
## Lag 5e+05            0.05121063            0.04227332           0.05695622
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000          1.000000000            1.0000000
## Lag 1e+05          0.179976352          0.212394287            0.3765100
## Lag 2e+05          0.088977676          0.098737482            0.2731099
## Lag 3e+05          0.068387847          0.090698080            0.2130957
## Lag 4e+05          0.049155556          0.036838165            0.1549889
## Lag 5e+05         -0.006275841          0.002159086            0.1016407
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000       1.00000000       1.00000000
## Lag 1e+05           0.19805419       0.25254939       0.10689701
## Lag 2e+05           0.09947109       0.11353291       0.04443078
## Lag 3e+05           0.05566930       0.07733932       0.04072907
## Lag 4e+05           0.04534273       0.05309509       0.04513220
## Lag 5e+05           0.03350735       0.04574283       0.01847718
## Chain 2 
##                 edges nodefactor.deg.main.deg.pers.0.1
## Lag 0      1.00000000                       1.00000000
## Lag 1e+05  0.18388150                       0.32355123
## Lag 2e+05  0.08526363                       0.19150208
## Lag 3e+05  0.04602199                       0.12733872
## Lag 4e+05  0.02648347                       0.07395077
## Lag 5e+05 -0.01538669                       0.02461530
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.036956381
## Lag 2e+05                     -0.005318630
## Lag 3e+05                     -0.003425924
## Lag 4e+05                      0.012687655
## Lag 5e+05                     -0.002604130
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.012620226
## Lag 2e+05                     -0.010653290
## Lag 3e+05                     -0.006287132
## Lag 4e+05                     -0.002058960
## Lag 5e+05                     -0.003371775
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.31715831
## Lag 2e+05                       0.15927738
## Lag 3e+05                       0.14183558
## Lag 4e+05                       0.09610526
## Lag 5e+05                       0.07650321
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.29581692                 NaN
## Lag 2e+05                       0.14373371                 NaN
## Lag 3e+05                       0.08125857                 NaN
## Lag 4e+05                       0.05168590                 NaN
## Lag 5e+05                       0.02069235                 NaN
##           nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0                     NaN        1.0000000000         1.000000000
## Lag 1e+05                 NaN       -0.0509709084         0.067533140
## Lag 2e+05                 NaN        0.0172206271         0.032721552
## Lag 3e+05                 NaN       -0.0001849933         0.007938125
## Lag 4e+05                 NaN       -0.0379547556         0.036701369
## Lag 5e+05                 NaN        0.0281328602         0.008961596
##           nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05        -0.028847232         0.007455700         0.014074056
## Lag 2e+05         0.037771097        -0.008180933        -0.009681983
## Lag 3e+05        -0.018654834        -0.005415666        -0.005512152
## Lag 4e+05        -0.009666338        -0.011719121         0.009589900
## Lag 5e+05        -0.017350602         0.006908394         0.001264134
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.21743383            0.27597940           0.26558438
## Lag 2e+05            0.11394589            0.15357584           0.15192521
## Lag 3e+05            0.05055204            0.12566279           0.07558147
## Lag 4e+05            0.04228884            0.07613763           0.06568469
## Lag 5e+05            0.03031518            0.04226778           0.05403955
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000           1.00000000
## Lag 1e+05          0.192036558           0.25541079           0.34907442
## Lag 2e+05          0.090557587           0.14065901           0.22980886
## Lag 3e+05          0.055057943           0.07141031           0.13374358
## Lag 4e+05          0.009604263           0.07059090           0.11065761
## Lag 5e+05         -0.006178966           0.04720275           0.06170796
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000       1.00000000       1.00000000
## Lag 1e+05           0.19194341       0.22532472       0.10088315
## Lag 2e+05           0.09553458       0.09751775       0.05022287
## Lag 3e+05           0.04361920       0.04978586       0.04871868
## Lag 4e+05           0.05740083       0.02560889       0.02397800
## Lag 5e+05           0.01025768      -0.01858163      -0.01114386
## Chain 3 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.19099306                       0.31394181
## Lag 2e+05 0.08666463                       0.18550314
## Lag 3e+05 0.03541982                       0.12274087
## Lag 4e+05 0.04276316                       0.07283654
## Lag 5e+05 0.03688249                       0.03865061
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.041039462
## Lag 2e+05                     -0.003906875
## Lag 3e+05                      0.001235230
## Lag 4e+05                     -0.020579219
## Lag 5e+05                      0.007290548
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.037010830
## Lag 2e+05                     -0.021914329
## Lag 3e+05                     -0.010457318
## Lag 4e+05                     -0.004992336
## Lag 5e+05                      0.007753687
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.30284169
## Lag 2e+05                       0.16415078
## Lag 3e+05                       0.10112971
## Lag 4e+05                       0.09915251
## Lag 5e+05                       0.05661441
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.30931148                 NaN
## Lag 2e+05                       0.17016238                 NaN
## Lag 3e+05                       0.09366197                 NaN
## Lag 4e+05                       0.07628022                 NaN
## Lag 5e+05                       0.07332734                 NaN
##           nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0                     NaN        1.0000000000        1.0000000000
## Lag 1e+05                 NaN        0.0003232322        0.1110566044
## Lag 2e+05                 NaN       -0.0141797992        0.0265608988
## Lag 3e+05                 NaN        0.0006166321        0.0164142620
## Lag 4e+05                 NaN        0.0033967501       -0.0001728478
## Lag 5e+05                 NaN       -0.0124968367        0.0025765887
##           nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05        -0.010173310        -0.015743026         0.006577369
## Lag 2e+05         0.019949399         0.008141449        -0.016195106
## Lag 3e+05         0.022639347         0.001144627        -0.025991440
## Lag 4e+05        -0.023194769        -0.034644483         0.020933308
## Lag 5e+05         0.003796121        -0.003652207         0.041496383
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.196306656            0.27438539           0.23375365
## Lag 2e+05           0.078773151            0.15230879           0.13239218
## Lag 3e+05           0.026621830            0.08693521           0.08730386
## Lag 4e+05           0.005144658            0.07761731           0.06546696
## Lag 5e+05           0.012521181            0.06285869           0.04066496
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000           1.00000000           1.00000000
## Lag 1e+05           0.19871703           0.27093868           0.38762181
## Lag 2e+05           0.06586543           0.13744210           0.23406586
## Lag 3e+05           0.02644668           0.08958688           0.17067470
## Lag 4e+05           0.02938314           0.04598522           0.12955626
## Lag 5e+05           0.01175612           0.01544252           0.07786773
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000       1.00000000      1.000000000
## Lag 1e+05           0.16943041       0.23859417      0.094788020
## Lag 2e+05           0.04522350       0.12558999      0.050654085
## Lag 3e+05           0.04783009       0.06057468      0.013602579
## Lag 4e+05           0.02820850       0.03995263     -0.000891723
## Lag 5e+05           0.01491930       0.04127578      0.017558434
## Chain 4 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.20381970                       0.34493185
## Lag 2e+05 0.07563581                       0.20130185
## Lag 3e+05 0.05627216                       0.13138160
## Lag 4e+05 0.04696646                       0.09607813
## Lag 5e+05 0.02640095                       0.08304586
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.043748960
## Lag 2e+05                      0.016336843
## Lag 3e+05                     -0.034303463
## Lag 4e+05                      0.002055959
## Lag 5e+05                      0.025557040
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0105757303
## Lag 2e+05                     0.0081555472
## Lag 3e+05                    -0.0008648638
## Lag 4e+05                    -0.0211591476
## Lag 5e+05                     0.0140501812
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.28853642
## Lag 2e+05                       0.15097628
## Lag 3e+05                       0.09049351
## Lag 4e+05                       0.05343287
## Lag 5e+05                       0.02665262
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.31401853                 NaN
## Lag 2e+05                       0.15580264                 NaN
## Lag 3e+05                       0.09599644                 NaN
## Lag 4e+05                       0.06615397                 NaN
## Lag 5e+05                       0.04150642                 NaN
##           nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0                     NaN         1.000000000        1.0000000000
## Lag 1e+05                 NaN         0.007447097        0.0855414617
## Lag 2e+05                 NaN         0.019582256        0.0107372129
## Lag 3e+05                 NaN        -0.010568010       -0.0001462219
## Lag 4e+05                 NaN         0.016879791       -0.0146598721
## Lag 5e+05                 NaN        -0.023449012        0.0133934348
##           nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0              1.00000000         1.000000000          1.00000000
## Lag 1e+05         -0.01042683        -0.004820614          0.03840360
## Lag 2e+05         -0.01252843        -0.013857310          0.04198657
## Lag 3e+05          0.01148737         0.004811532          0.01607645
## Lag 4e+05         -0.00438014        -0.038301198          0.02163643
## Lag 5e+05          0.00657696        -0.021707675          0.01647095
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.21207899            0.27720147           0.26115571
## Lag 2e+05            0.13052454            0.13017927           0.13442124
## Lag 3e+05            0.08384138            0.09694105           0.09832309
## Lag 4e+05            0.07124823            0.08448462           0.06872138
## Lag 5e+05            0.04326489            0.03384386           0.04193409
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000            1.0000000
## Lag 1e+05          0.202768955           0.27528665            0.3762952
## Lag 2e+05          0.075770329           0.12129889            0.2364874
## Lag 3e+05          0.038338719           0.05354016            0.1598294
## Lag 4e+05          0.031664424           0.03022846            0.1381709
## Lag 5e+05          0.004408722           0.03849380            0.0907154
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000       1.00000000      1.000000000
## Lag 1e+05           0.18890589       0.22853867      0.126792028
## Lag 2e+05           0.07837628       0.08661773      0.024394828
## Lag 3e+05           0.04689467       0.05215740      0.021581356
## Lag 4e+05           0.03514535       0.04500449      0.030138139
## Lag 5e+05           0.02532596       0.03309644     -0.006737163
## Chain 5 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.26322366                       0.34963847
## Lag 2e+05 0.12805167                       0.19680951
## Lag 3e+05 0.09900166                       0.14073952
## Lag 4e+05 0.05327681                       0.09704682
## Lag 5e+05 0.04357803                       0.06100201
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                       0.06418533
## Lag 2e+05                       0.01204802
## Lag 3e+05                       0.03007316
## Lag 4e+05                      -0.01015905
## Lag 5e+05                       0.01345998
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                     -0.003469021
## Lag 2e+05                     -0.019135558
## Lag 3e+05                     -0.008525964
## Lag 4e+05                      0.010091272
## Lag 5e+05                      0.005924479
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.30770957
## Lag 2e+05                       0.18073830
## Lag 3e+05                       0.11857333
## Lag 4e+05                       0.08389305
## Lag 5e+05                       0.05823593
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.31483963                 NaN
## Lag 2e+05                       0.16009940                 NaN
## Lag 3e+05                       0.11984356                 NaN
## Lag 4e+05                       0.06841642                 NaN
## Lag 5e+05                       0.04808126                 NaN
##           nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0                     NaN         1.000000000          1.00000000
## Lag 1e+05                 NaN         0.003698073          0.12889163
## Lag 2e+05                 NaN         0.012692547          0.04062597
## Lag 3e+05                 NaN         0.027675974          0.02027939
## Lag 4e+05                 NaN        -0.022461708          0.01014868
## Lag 5e+05                 NaN        -0.012870842          0.02027598
##           nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05         0.008123602        -0.008209168         0.020872855
## Lag 2e+05         0.017167548         0.013236745        -0.003537093
## Lag 3e+05         0.029501997        -0.009030108         0.006725842
## Lag 4e+05         0.001643124        -0.009019656         0.014109225
## Lag 5e+05         0.037533606         0.005658274         0.001223704
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.24842161            0.30887311           0.26398249
## Lag 2e+05            0.11809260            0.16526930           0.18470749
## Lag 3e+05            0.05698473            0.09648213           0.10306572
## Lag 4e+05            0.06073904            0.08783392           0.09482083
## Lag 5e+05            0.05890767            0.06157450           0.06548860
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000           1.00000000            1.0000000
## Lag 1e+05           0.20416669           0.25466160            0.3779820
## Lag 2e+05           0.09348851           0.14472450            0.2121703
## Lag 3e+05           0.05433239           0.07126926            0.1423489
## Lag 4e+05           0.03554781           0.06054000            0.1385701
## Lag 5e+05           0.03359375           0.03396679            0.1051276
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000       1.00000000       1.00000000
## Lag 1e+05           0.20900398       0.29614254       0.16677127
## Lag 2e+05           0.09792873       0.14687658       0.06336575
## Lag 3e+05           0.08224240       0.13004955       0.05534394
## Lag 4e+05           0.01918979       0.07404614       0.04040475
## Lag 5e+05           0.01154144       0.05007448       0.01194436
## Chain 6 
##                 edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.000000000                       1.00000000
## Lag 1e+05 0.196737981                       0.32087898
## Lag 2e+05 0.109445949                       0.17277115
## Lag 3e+05 0.055800861                       0.10563041
## Lag 4e+05 0.005539528                       0.05698560
## Lag 5e+05 0.020236940                       0.03632713
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                           1.00000000
## Lag 1e+05                       0.08284264
## Lag 2e+05                       0.01390810
## Lag 3e+05                       0.01166647
## Lag 4e+05                       0.02499404
## Lag 5e+05                      -0.01585042
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.020707027
## Lag 2e+05                     -0.007047753
## Lag 3e+05                     -0.002833084
## Lag 4e+05                      0.002199343
## Lag 5e+05                     -0.022387111
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.32897250
## Lag 2e+05                       0.16453049
## Lag 3e+05                       0.11725015
## Lag 4e+05                       0.07224970
## Lag 5e+05                       0.02029036
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.32798588                 NaN
## Lag 2e+05                       0.18787842                 NaN
## Lag 3e+05                       0.09916599                 NaN
## Lag 4e+05                       0.04036421                 NaN
## Lag 5e+05                       0.03356601                 NaN
##           nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0                     NaN         1.000000000         1.000000000
## Lag 1e+05                 NaN         0.030549838         0.092146476
## Lag 2e+05                 NaN        -0.013837255         0.024044717
## Lag 3e+05                 NaN         0.021133086        -0.013529454
## Lag 4e+05                 NaN        -0.011658025        -0.002811230
## Lag 5e+05                 NaN         0.006286412        -0.002560422
##           nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0             1.000000000         1.000000000         1.000000000
## Lag 1e+05         0.033746441         0.036998231        -0.006527560
## Lag 2e+05        -0.008098698         0.014464298        -0.009669324
## Lag 3e+05         0.016248517        -0.013653410        -0.005182837
## Lag 4e+05         0.009267286        -0.004542760        -0.025725314
## Lag 5e+05        -0.018179530        -0.007545292         0.008523293
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000            1.0000000
## Lag 1e+05            0.20980540            0.31258817            0.2705241
## Lag 2e+05            0.10272850            0.18585561            0.1924664
## Lag 3e+05            0.04979621            0.11392135            0.1174185
## Lag 4e+05            0.01319342            0.07599504            0.1102414
## Lag 5e+05            0.03405885            0.07847373            0.0571432
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0               1.00000000           1.00000000            1.0000000
## Lag 1e+05           0.16383993           0.26703134            0.3863044
## Lag 2e+05           0.09754358           0.12730479            0.2524307
## Lag 3e+05           0.03840430           0.06587753            0.1999117
## Lag 4e+05           0.01813591           0.03097604            0.1440775
## Lag 5e+05           0.00364068           0.06204510            0.1281311
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0             1.0000000000       1.00000000      1.000000000
## Lag 1e+05         0.1828939335       0.24611213      0.118987747
## Lag 2e+05         0.0795499094       0.15194240      0.066006320
## Lag 3e+05         0.0277291846       0.07713354      0.038746863
## Lag 4e+05        -0.0364320172       0.03012264     -0.002049392
## Lag 5e+05         0.0003647182       0.03000120     -0.001565757
## Chain 7 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.21271648                       0.32762795
## Lag 2e+05 0.09388585                       0.18369446
## Lag 3e+05 0.05741240                       0.11909313
## Lag 4e+05 0.03411777                       0.08762800
## Lag 5e+05 0.01005790                       0.06443821
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                         1.0000000000
## Lag 1e+05                     0.0402359137
## Lag 2e+05                     0.0335015993
## Lag 3e+05                     0.0009190991
## Lag 4e+05                     0.0162460178
## Lag 5e+05                    -0.0060420610
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                          1.000000000
## Lag 1e+05                      0.017534063
## Lag 2e+05                      0.030580469
## Lag 3e+05                     -0.004290301
## Lag 4e+05                      0.017491447
## Lag 5e+05                     -0.016034924
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.31829795
## Lag 2e+05                       0.21754567
## Lag 3e+05                       0.13608512
## Lag 4e+05                       0.05590445
## Lag 5e+05                       0.05483424
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.31007120                 NaN
## Lag 2e+05                       0.16693452                 NaN
## Lag 3e+05                       0.11183970                 NaN
## Lag 4e+05                       0.05391883                 NaN
## Lag 5e+05                       0.04151056                 NaN
##           nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0                     NaN         1.000000000         1.000000000
## Lag 1e+05                 NaN        -0.007409601         0.119128549
## Lag 2e+05                 NaN        -0.005049754         0.045514395
## Lag 3e+05                 NaN         0.009320482         0.006768883
## Lag 4e+05                 NaN        -0.006064997         0.002526589
## Lag 5e+05                 NaN         0.024509510        -0.017557989
##           nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0             1.000000000        1.0000000000         1.000000000
## Lag 1e+05        -0.009357734       -0.0015865068         0.012673082
## Lag 2e+05         0.009347286       -0.0017059215        -0.011293538
## Lag 3e+05        -0.003961816        0.0006515518         0.010243247
## Lag 4e+05         0.004771873        0.0116216826        -0.005023963
## Lag 5e+05        -0.007313439       -0.0003422546         0.000879657
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0                1.00000000            1.00000000           1.00000000
## Lag 1e+05            0.20323118            0.27430986           0.29014167
## Lag 2e+05            0.08033961            0.16289757           0.17577330
## Lag 3e+05            0.06753307            0.10715994           0.09817749
## Lag 4e+05            0.05309927            0.08314507           0.06573527
## Lag 5e+05            0.02179144            0.04947550           0.05646113
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0              1.000000000           1.00000000            1.0000000
## Lag 1e+05          0.188011229           0.25496439            0.3494473
## Lag 2e+05          0.069245254           0.11263445            0.2323210
## Lag 3e+05          0.049888509           0.05222471            0.1772063
## Lag 4e+05          0.024037634           0.04145821            0.1383972
## Lag 5e+05         -0.002349084           0.00173860            0.1153657
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0               1.00000000       1.00000000      1.000000000
## Lag 1e+05           0.19230286       0.26014676      0.125499369
## Lag 2e+05           0.08358311       0.12761984      0.041046395
## Lag 3e+05           0.05413363       0.08140981      0.051124703
## Lag 4e+05           0.03766015       0.04240342      0.002972602
## Lag 5e+05           0.01717837       0.02121709      0.005782717
## Chain 8 
##                edges nodefactor.deg.main.deg.pers.0.1
## Lag 0     1.00000000                       1.00000000
## Lag 1e+05 0.20478664                       0.33308118
## Lag 2e+05 0.09042746                       0.20507179
## Lag 3e+05 0.05870428                       0.12017288
## Lag 4e+05 0.02444014                       0.04867614
## Lag 5e+05 0.00638726                       0.03128094
##           nodefactor.deg.main.deg.pers.0.2
## Lag 0                          1.000000000
## Lag 1e+05                      0.056391924
## Lag 2e+05                     -0.016721866
## Lag 3e+05                      0.009254921
## Lag 4e+05                     -0.013403892
## Lag 5e+05                      0.005742263
##           nodefactor.deg.main.deg.pers.1.0
## Lag 0                         1.0000000000
## Lag 1e+05                    -0.0171879131
## Lag 2e+05                     0.0004579701
## Lag 3e+05                     0.0023681853
## Lag 4e+05                    -0.0021014551
## Lag 5e+05                    -0.0066607730
##           nodefactor.deg.main.deg.pers.1.1
## Lag 0                           1.00000000
## Lag 1e+05                       0.30875010
## Lag 2e+05                       0.14992387
## Lag 3e+05                       0.09106860
## Lag 4e+05                       0.05161501
## Lag 5e+05                       0.05425171
##           nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0                           1.00000000                 NaN
## Lag 1e+05                       0.30513367                 NaN
## Lag 2e+05                       0.16269995                 NaN
## Lag 3e+05                       0.08824312                 NaN
## Lag 4e+05                       0.08624662                 NaN
## Lag 5e+05                       0.06264501                 NaN
##           nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0                     NaN        1.0000000000          1.00000000
## Lag 1e+05                 NaN       -0.0025304070          0.10947087
## Lag 2e+05                 NaN        0.0003365124          0.03699104
## Lag 3e+05                 NaN       -0.0365369547          0.01663162
## Lag 4e+05                 NaN        0.0005436734          0.01656792
## Lag 5e+05                 NaN       -0.0127049707         -0.01500576
##           nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0             1.000000000         1.000000000          1.00000000
## Lag 1e+05        -0.008687854         0.006495078          0.01134886
## Lag 2e+05        -0.013324973        -0.005250035          0.02062511
## Lag 3e+05         0.020756306         0.014293206          0.01191247
## Lag 4e+05        -0.013087419        -0.022880467          0.01863942
## Lag 5e+05        -0.015383674        -0.012730433         -0.01691345
##           nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0               1.000000000            1.00000000           1.00000000
## Lag 1e+05           0.218027320            0.28051393           0.26873391
## Lag 2e+05           0.070311902            0.16076626           0.15729928
## Lag 3e+05           0.061159868            0.09620750           0.09092343
## Lag 4e+05          -0.005816823            0.05425449           0.09236924
## Lag 5e+05           0.005083985            0.02042748           0.07932336
##           nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0             1.000000e+00           1.00000000           1.00000000
## Lag 1e+05         2.212740e-01           0.26616465           0.37421701
## Lag 2e+05         8.898642e-02           0.16352736           0.25447075
## Lag 3e+05         4.630568e-02           0.11128618           0.17277292
## Lag 4e+05         3.827191e-02           0.07677626           0.11106935
## Lag 5e+05         1.687697e-05           0.06547522           0.05734364
##           nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0              1.000000000       1.00000000      1.000000000
## Lag 1e+05          0.170779563       0.25140321      0.110559955
## Lag 2e+05          0.062795813       0.10409109      0.022679230
## Lag 3e+05          0.032425599       0.05422549      0.003049577
## Lag 4e+05          0.008921422       0.01566549     -0.001846504
## Lag 5e+05          0.007504089       0.02137539      0.006181581
## 
## Sample statistics burn-in diagnostic (Geweke):
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.

## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.35977                         -0.25864 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.66514                         -0.22960 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.76158                         -0.82967 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                          0.02204                          0.93421 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                         -0.65606                         -0.79246 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                         -0.63269                          0.57698 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          1.37832                          0.59875 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -1.30730                         -0.03470 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          0.48429                         -2.10850 
##                 nodematch.region                 absdiff.sqrt.age 
##                         -0.02003                         -0.51141 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.71902169                       0.79591663 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.50596233                       0.81840590 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.44631274                       0.40672803 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                       0.98241491                       0.35019558 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                       0.51178421                       0.42809134 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                       0.52693610                       0.56395282 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.16810458                       0.54933941 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.19111149                       0.97232263 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.62817994                       0.03498801 
##                 nodematch.region                 absdiff.sqrt.age 
##                       0.98401663                       0.60906483 
## Joint P-value (lower = worse):  1 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.43175                         -2.65103 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          1.88506                          0.91395 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          2.29948                          2.22466 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                          0.35977                          0.08879 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                         -0.39507                         -0.34730 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                          1.31724                          0.75197 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                         -0.06923                         -1.49686 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -0.49181                          0.18743 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -1.73754                         -0.21735 
##                 nodematch.region                 absdiff.sqrt.age 
##                          0.91437                         -0.27516 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                      0.665925378                      0.008024661 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                      0.059421488                      0.360745073 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                      0.021477736                      0.026103920 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                      0.719020939                      0.929252347 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                      0.692789079                      0.728364793 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                      0.187757394                      0.452066963 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                      0.944803780                      0.134429514 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                      0.622851387                      0.851321222 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                      0.082291377                      0.827939139 
##                 nodematch.region                 absdiff.sqrt.age 
##                      0.360522179                      0.783191955 
## Joint P-value (lower = worse):  0.002602775 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         0.497347                         1.104568 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        -0.114861                        -0.241551 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        -0.410172                        -0.491445 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                        -0.308175                        -0.248053 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                         0.757081                        -1.391366 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                        -0.035174                        -1.854815 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                        -0.062057                         0.904253 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         0.004068                        -2.323121 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         0.733962                         1.552451 
##                 nodematch.region                 absdiff.sqrt.age 
##                         0.130896                         0.554196 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.61894443                       0.26934670 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.90855496                       0.80912818 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.68168008                       0.62311201 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                       0.75794905                       0.80409350 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                       0.44900157                       0.16411441 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                       0.97194123                       0.06362269 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.95051783                       0.36586122 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.99675394                       0.02017267 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.46297174                       0.12055445 
##                 nodematch.region                 absdiff.sqrt.age 
##                       0.89585735                       0.57944457 
## Joint P-value (lower = worse):  0.5778305 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                         -0.61163                         -1.83653 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.91592                          0.05771 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          1.96381                          0.65141 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                         -0.90643                         -1.29679 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                         -0.25577                          1.53631 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                         -0.92837                         -1.04431 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          0.71199                         -0.96415 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -1.37408                         -0.72397 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          1.59071                         -0.36997 
##                 nodematch.region                 absdiff.sqrt.age 
##                         -0.11652                         -0.61764 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.54078181                       0.06627883 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.35970969                       0.95398007 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.04955223                       0.51477965 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                       0.36470846                       0.19470412 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                       0.79812986                       0.12446347 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                       0.35321773                       0.29633998 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.47646820                       0.33497154 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.16941726                       0.46908624 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.11167516                       0.71140619 
##                 nodematch.region                 absdiff.sqrt.age 
##                       0.90723921                       0.53681079 
## Joint P-value (lower = worse):  0.07251787 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          1.36609                          1.40760 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -2.76873                         -0.00147 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.43890                          0.05609 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                          2.12012                         -0.86632 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                          0.81795                         -0.39212 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                         -0.84273                          1.55267 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                         -0.30832                         -1.17266 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          0.18378                         -0.65369 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          0.08796                          1.62517 
##                 nodematch.region                 absdiff.sqrt.age 
##                          2.19649                          1.32998 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                      0.171910051                      0.159249243 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                      0.005627449                      0.998827089 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                      0.660735069                      0.955272608 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                      0.033996241                      0.386314680 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                      0.413386808                      0.694973235 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                      0.399377086                      0.120502789 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                      0.757841196                      0.240933804 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                      0.854189144                      0.513309588 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                      0.929911978                      0.104126997 
##                 nodematch.region                 absdiff.sqrt.age 
##                      0.028056952                      0.183523699 
## Joint P-value (lower = worse):  1.99522e-05 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.45346                          0.21544 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.79415                         -0.09743 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          0.23924                          0.63003 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                          0.44566                          1.51245 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                         -0.66797                         -1.45091 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                         -0.64993                         -0.78657 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                         -1.11427                         -0.77462 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          0.20048                         -0.46746 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                         -0.13284                          1.74283 
##                 nodematch.region                 absdiff.sqrt.age 
##                          0.87655                          1.30865 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.65021697                       0.82942345 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.42710750                       0.92238196 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.81091897                       0.52867294 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                       0.65584158                       0.13042032 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                       0.50415196                       0.14680546 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                       0.51573466                       0.43153098 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.26516408                       0.43856429 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.84110352                       0.64017201 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.89432123                       0.08136331 
##                 nodematch.region                 absdiff.sqrt.age 
##                       0.38073137                       0.19065171 
## Joint P-value (lower = worse):  0.2812042 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.76164                          0.82730 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                         -0.55628                         -1.19049 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                          1.63246                         -1.12443 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                         -0.12668                          1.50833 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                         -0.91231                          0.50026 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                          0.54102                         -0.47719 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          0.06583                          1.62955 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                         -1.01388                         -0.21770 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          0.47254                          1.41034 
##                 nodematch.region                 absdiff.sqrt.age 
##                          1.13479                          0.60257 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                        0.4462766                        0.4080698 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                        0.5780168                        0.2338554 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                        0.1025833                        0.2608316 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                        0.8991958                        0.1314706 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                        0.3616077                        0.6168903 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                        0.5884905                        0.6332281 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                        0.9475156                        0.1031972 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                        0.3106410                        0.8276630 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                        0.6365385                        0.1584385 
##                 nodematch.region                 absdiff.sqrt.age 
##                        0.2564653                        0.5467969 
## Joint P-value (lower = worse):  0.3569499 .
## Chain 8 
## 
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5 
## 
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                          0.32005                          0.16278 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                          0.10403                         -1.28204 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                         -0.02223                         -0.14860 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                          0.00000                          0.16310 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                          0.06042                         -0.22094 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                         -3.09206                         -1.33016 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                          0.31502                          0.95794 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                          0.68062                         -1.03498 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                          0.99788                          0.70932 
##                 nodematch.region                 absdiff.sqrt.age 
##                         -0.02342                          0.01909 
## 
## Individual P-values (lower = worse):
##                            edges nodefactor.deg.main.deg.pers.0.1 
##                       0.74892678                       0.87068912 
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0 
##                       0.91714746                       0.19982780 
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2 
##                       0.98226497                       0.88186994 
##              nodefactor.riskg.O1              nodefactor.riskg.O2 
##                              NaN                              NaN 
##              nodefactor.riskg.O3              nodefactor.riskg.O4 
##                       1.00000000                       0.87044004 
##              nodefactor.riskg.Y1              nodefactor.riskg.Y2 
##                       0.95182405                       0.82514157 
##              nodefactor.riskg.Y3            nodefactor.race..wa.B 
##                       0.00198773                       0.18346524 
##            nodefactor.race..wa.H             nodefactor.region.EW 
##                       0.75274979                       0.33809178 
##             nodefactor.region.OW             nodematch.race..wa.B 
##                       0.49611236                       0.30067984 
##             nodematch.race..wa.H             nodematch.race..wa.O 
##                       0.31833748                       0.47812722 
##                 nodematch.region                 absdiff.sqrt.age 
##                       0.98131516                       0.98477074 
## Joint P-value (lower = worse):  0.006832838 .
## Warning in formals(fun): argument is not a function

## 
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).

Summary of model fit

Model 1

summary(est.i.buildup.bal[[1]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55bdbcf8b970>
## 
## Iterations:  2 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                  -11.48530    0.04581      0  <1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0  <1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0  <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 2

summary(est.i.buildup.bal[[2]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55bdd82cae28>
## 
## Iterations:  2 out of 400 
## 
## Monte Carlo MLE Results:
##                         Estimate Std. Error MCMC % p-value    
## edges                  -11.65068    0.05800      0 < 1e-04 ***
## nodefactor.race..wa.B    0.34267    0.12152      0 0.00481 ** 
## nodefactor.race..wa.H    0.44886    0.08959      0 < 1e-04 ***
## nodematch.role.class.I      -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R      -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 3

summary(est.i.buildup.bal[[3]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa", 
##     diff = TRUE) + offset(nodematch("role.class", diff = TRUE, 
##     keep = 1:2))
## <environment: 0x55bdee0f1e08>
## 
## Iterations:  2 out of 400 
## 
## Monte Carlo MLE Results:
##                        Estimate Std. Error MCMC % p-value    
## edges                  -12.1436     0.2967      0 < 1e-04 ***
## nodefactor.race..wa.B    0.7643     0.2648      0 0.00390 ** 
## nodefactor.race..wa.H    0.8665     0.2812      0 0.00206 ** 
## nodematch.race..wa.B    -0.5122     0.6963      0 0.46191    
## nodematch.race..wa.H    -0.2075     0.4094      0 0.61225    
## nodematch.race..wa.O     0.5148     0.3024      0 0.08872 .  
## nodematch.role.class.I     -Inf     0.0000      0 < 1e-04 ***
## nodematch.role.class.R     -Inf     0.0000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 4

summary(est.i.buildup.bal[[4]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa", 
##     base = 3) + nodematch("race..wa", diff = TRUE) + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55be041a57e0>
## 
## Iterations:  2 out of 400 
## 
## Monte Carlo MLE Results:
##                                   Estimate Std. Error MCMC % p-value    
## edges                            -11.94315    0.30323      0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1   0.89117    0.08973      0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2  -0.80008    0.17229      0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0  -2.22091    0.16865      0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1   0.74878    0.09862      0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2   0.78870    0.09545      0 < 1e-04 ***
## nodefactor.race..wa.B              0.72925    0.26123      0 0.00524 ** 
## nodefactor.race..wa.H              0.90359    0.27964      0 0.00123 ** 
## nodematch.race..wa.B              -0.51085    0.68486      0 0.45572    
## nodematch.race..wa.H              -0.20830    0.40524      0 0.60723    
## nodematch.race..wa.O               0.51492    0.30096      0 0.08709 .  
## nodematch.role.class.I                -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R                -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 5

summary(est.i.buildup.bal[[5]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + offset(nodematch("role.class", diff = TRUE, 
##     keep = 1:2))
## <environment: 0x55be1a4ef778>
## 
## Iterations:  2 out of 400 
## 
## Monte Carlo MLE Results:
##                                   Estimate Std. Error MCMC %  p-value    
## edges                            -11.59388    0.30997      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1   0.89312    0.09046      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2  -0.81131    0.17350      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0  -2.25532    0.17056      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1   0.70708    0.09916      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2   0.75409    0.09641      0  < 1e-04 ***
## nodefactor.race..wa.B              0.68515    0.26507      0 0.009743 ** 
## nodefactor.race..wa.H              0.94696    0.28314      0 0.000824 ***
## nodefactor.region.EW              -0.39073    0.11825      0 0.000952 ***
## nodefactor.region.OW              -0.43996    0.07620      0  < 1e-04 ***
## nodematch.race..wa.B              -0.51373    0.68614      0 0.454026    
## nodematch.race..wa.H              -0.21283    0.40358      0 0.597951    
## nodematch.race..wa.O               0.51908    0.30414      0 0.087872 .  
## nodematch.role.class.I                -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.R                -Inf    0.00000      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 6

summary(est.i.buildup.bal[[6]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa", 
##     base = 3) + nodefactor("region", base = 2) + nodematch("race..wa", 
##     diff = TRUE) + absdiff("sqrt.age") + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55be3094b810>
## 
## Iterations:  2 out of 400 
## 
## Monte Carlo MLE Results:
##                                   Estimate Std. Error MCMC %  p-value    
## edges                            -10.98360    0.31398      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1   0.89776    0.08986      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2  -0.81143    0.17262      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0  -2.25785    0.16968      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1   0.71330    0.09909      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2   0.75700    0.09615      0  < 1e-04 ***
## nodefactor.race..wa.B              0.67961    0.26428      0 0.010123 *  
## nodefactor.race..wa.H              0.94895    0.28142      0 0.000746 ***
## nodefactor.region.EW              -0.39309    0.11725      0 0.000801 ***
## nodefactor.region.OW              -0.43980    0.07551      0  < 1e-04 ***
## nodematch.race..wa.B              -0.51131    0.69248      0 0.460282    
## nodematch.race..wa.H              -0.20997    0.40483      0 0.603993    
## nodematch.race..wa.O               0.51619    0.30258      0 0.088017 .  
## absdiff.sqrt.age                  -0.63705    0.06875      0  < 1e-04 ***
## nodematch.role.class.I                -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.R                -Inf    0.00000      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 7

summary(est.i.buildup.bal[[7]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("riskg") + 
##     nodefactor("race..wa", base = 3) + nodefactor("region", base = 2) + 
##     nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") + 
##     offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55be4d0f8ec8>
## 
## Iterations:  3 out of 400 
## 
## Monte Carlo MLE Results:
##                                   Estimate Std. Error MCMC %  p-value    
## edges                             -6.39857    0.30979      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1   0.95094    0.09062      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2  -0.91826    0.17278      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0  -2.36303    0.17068      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1   0.71388    0.09814      0  < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2   0.95482    0.09756      0  < 1e-04 ***
## nodefactor.riskg.O2              -18.81234         NA     NA       NA    
## nodefactor.riskg.O3              -18.78460         NA     NA       NA    
## nodefactor.riskg.O4               -4.81294    0.38712      0  < 1e-04 ***
## nodefactor.riskg.Y1               -3.57404    0.14262      0  < 1e-04 ***
## nodefactor.riskg.Y2               -7.95999    0.86162      0  < 1e-04 ***
## nodefactor.riskg.Y3               -6.17949    0.36565      0  < 1e-04 ***
## nodefactor.riskg.Y4               -4.03144    0.15921      0  < 1e-04 ***
## nodefactor.race..wa.B              1.20301    0.26386      0  < 1e-04 ***
## nodefactor.race..wa.H              1.10908    0.27895      0  < 1e-04 ***
## nodefactor.region.EW              -0.28311    0.11867      0 0.017051 *  
## nodefactor.region.OW              -0.51623    0.07621      0  < 1e-04 ***
## nodematch.race..wa.B              -0.51222    0.69673      0 0.462235    
## nodematch.race..wa.H              -0.19558    0.40117      0 0.625891    
## nodematch.race..wa.O               0.53630    0.30210      0 0.075859 .  
## absdiff.sqrt.age                   0.26781    0.07545      0 0.000386 ***
## nodematch.role.class.I                -Inf    0.00000      0  < 1e-04 ***
## nodematch.role.class.R                -Inf    0.00000      0  < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Model 8

summary(est.i.buildup.bal[[8]])
## 
## ==========================
## Summary of model fit
## ==========================
## 
## Formula:   nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("riskg", 
##     base = 8) + nodefactor("race..wa", base = 3) + nodefactor("region", 
##     base = 2) + nodematch("race..wa", diff = TRUE) + nodematch("region", 
##     diff = FALSE) + absdiff("sqrt.age") + offset(nodematch("role.class", 
##     diff = TRUE, keep = 1:2))
## <environment: 0x55be670ea678>
## 
## Iterations:  2 out of 400 
## 
## Monte Carlo MLE Results:
##                                   Estimate Std. Error MCMC % p-value    
## edges                             -9.69942    0.33331      0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1   0.98692    0.08954      0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2  -0.74837    0.17207      0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0  -2.22953    0.16949      0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1   0.80517    0.10004      0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2   0.76315    0.09568      0 < 1e-04 ***
## nodefactor.riskg.O1              -17.19841         NA     NA      NA    
## nodefactor.riskg.O2              -17.29306         NA     NA      NA    
## nodefactor.riskg.O3               -3.34355    0.37048      0 < 1e-04 ***
## nodefactor.riskg.O4               -0.52291    0.09667      0 < 1e-04 ***
## nodefactor.riskg.Y1               -6.34413    0.84913      0 < 1e-04 ***
## nodefactor.riskg.Y2               -4.53215    0.35023      0 < 1e-04 ***
## nodefactor.riskg.Y3               -2.40631    0.12476      0 < 1e-04 ***
## nodefactor.race..wa.B              0.51364    0.26614      0 0.05361 .  
## nodefactor.race..wa.H              0.90836    0.28387      0 0.00137 ** 
## nodefactor.region.EW               0.17206    0.10230      0 0.09258 .  
## nodefactor.region.OW              -0.11012    0.06073      0 0.06978 .  
## nodematch.race..wa.B              -0.42303    0.57415      0 0.46124    
## nodematch.race..wa.H              -0.26994    0.40518      0 0.50527    
## nodematch.race..wa.O               0.54402    0.30539      0 0.07485 .  
## nodematch.region                   1.74468    0.11978      0 < 1e-04 ***
## absdiff.sqrt.age                  -0.58753    0.07097      0 < 1e-04 ***
## nodematch.role.class.I                -Inf    0.00000      0 < 1e-04 ***
## nodematch.role.class.R                -Inf    0.00000      0 < 1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run 
##   > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
## 
##  The following terms are fixed by offset and are not estimated:
##   nodematch.role.class.I nodematch.role.class.R 
## 
## 
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -Inf

Network diagnostics

Model 1

(dx_inst1 <- netdx(est.i.buildup.bal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            479.159  479.015        0 21.817
## nodefactor.deg.main.deg.pers.0.1      NA   63.905       NA  8.248
## nodefactor.deg.main.deg.pers.0.2      NA   73.868       NA  8.839
## nodefactor.deg.main.deg.pers.1.0      NA  312.773       NA 20.538
## nodefactor.deg.main.deg.pers.1.1      NA   57.717       NA  7.752
## nodefactor.deg.main.deg.pers.1.2      NA   58.685       NA  7.919
## nodefactor.riskg.O1                   NA   54.780       NA  7.567
## nodefactor.riskg.O2                   NA   54.490       NA  7.637
## nodefactor.riskg.O3                   NA   55.027       NA  7.663
## nodefactor.riskg.O4                   NA   54.761       NA  7.647
## nodefactor.riskg.Y1                   NA  184.416       NA 14.698
## nodefactor.riskg.Y2                   NA  184.435       NA 14.785
## nodefactor.riskg.Y3                   NA  184.883       NA 15.022
## nodefactor.race..wa.B                 NA   58.125       NA  7.840
## nodefactor.race..wa.H                 NA  103.389       NA 10.759
## nodefactor.region.EW                  NA   96.802       NA 10.309
## nodefactor.region.OW                  NA  313.245       NA 20.235
## nodematch.race..wa.B                  NA    1.774       NA  1.343
## nodematch.race..wa.H                  NA    5.570       NA  2.387
## nodematch.race..wa.O                  NA  331.161       NA 18.350
## nodematch.region                      NA  212.770       NA 14.471
## absdiff.sqrt.age                      NA  547.666       NA 30.597
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst1, type="formation")

plot(dx_inst1, type="duration")

plot(dx_inst1, type="dissolution")

Model 2

(dx_inst2 <- netdx(est.i.buildup.bal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            479.159  479.033    0.000 21.914
## nodefactor.deg.main.deg.pers.0.1      NA   63.842       NA  8.277
## nodefactor.deg.main.deg.pers.0.2      NA   72.970       NA  9.006
## nodefactor.deg.main.deg.pers.1.0      NA  315.375       NA 20.467
## nodefactor.deg.main.deg.pers.1.1      NA   57.628       NA  7.791
## nodefactor.deg.main.deg.pers.1.2      NA   59.589       NA  7.954
## nodefactor.riskg.O1                   NA   54.605       NA  7.587
## nodefactor.riskg.O2                   NA   54.485       NA  7.699
## nodefactor.riskg.O3                   NA   54.880       NA  7.702
## nodefactor.riskg.O4                   NA   55.430       NA  7.656
## nodefactor.riskg.Y1                   NA  185.282       NA 14.928
## nodefactor.riskg.Y2                   NA  183.891       NA 14.863
## nodefactor.riskg.Y3                   NA  185.000       NA 14.831
## nodefactor.race..wa.B             75.591   75.789    0.003  9.040
## nodefactor.race..wa.H            149.174  149.332    0.001 13.165
## nodefactor.region.EW                  NA  100.611       NA 10.622
## nodefactor.region.OW                  NA  309.917       NA 20.284
## nodematch.race..wa.B                  NA    3.001       NA  1.729
## nodematch.race..wa.H                  NA   11.634       NA  3.483
## nodematch.race..wa.O                  NA  280.387       NA 16.720
## nodematch.region                      NA  211.892       NA 14.469
## absdiff.sqrt.age                      NA  547.260       NA 30.689
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst2, type="formation")

plot(dx_inst2, type="duration")

plot(dx_inst2, type="dissolution")

Model 3

(dx_inst3 <- netdx(est.i.buildup.bal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            479.159  478.979    0.000 21.831
## nodefactor.deg.main.deg.pers.0.1      NA   63.997       NA  8.249
## nodefactor.deg.main.deg.pers.0.2      NA   73.099       NA  8.947
## nodefactor.deg.main.deg.pers.1.0      NA  315.262       NA 20.178
## nodefactor.deg.main.deg.pers.1.1      NA   57.640       NA  7.838
## nodefactor.deg.main.deg.pers.1.2      NA   59.599       NA  7.914
## nodefactor.riskg.O1                   NA   54.506       NA  7.583
## nodefactor.riskg.O2                   NA   54.413       NA  7.534
## nodefactor.riskg.O3                   NA   54.860       NA  7.655
## nodefactor.riskg.O4                   NA   55.426       NA  7.739
## nodefactor.riskg.Y1                   NA  185.198       NA 14.778
## nodefactor.riskg.Y2                   NA  183.714       NA 14.556
## nodefactor.riskg.Y3                   NA  185.269       NA 14.852
## nodefactor.race..wa.B             75.591   75.512   -0.001  9.134
## nodefactor.race..wa.H            149.174  149.098   -0.001 13.200
## nodefactor.region.EW                  NA  100.762       NA 10.538
## nodefactor.region.OW                  NA  309.919       NA 20.129
## nodematch.race..wa.B               2.540    2.524   -0.006  1.583
## nodematch.race..wa.H              13.269   13.301    0.002  3.650
## nodematch.race..wa.O             286.880  286.773    0.000 16.954
## nodematch.region                      NA  211.646       NA 14.672
## absdiff.sqrt.age                      NA  547.557       NA 30.698
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst3, type="formation")

plot(dx_inst3, type="duration")

plot(dx_inst3, type="dissolution")

Model 4

(dx_inst4 <- netdx(est.i.buildup.bal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            479.159  478.971    0.000 22.061
## nodefactor.deg.main.deg.pers.0.1 172.310  172.246    0.000 14.255
## nodefactor.deg.main.deg.pers.0.2  36.371   36.415    0.001  6.110
## nodefactor.deg.main.deg.pers.1.0  38.033   38.020    0.000  6.297
## nodefactor.deg.main.deg.pers.1.1 135.538  135.422   -0.001 12.513
## nodefactor.deg.main.deg.pers.1.2 145.388  145.149   -0.002 12.863
## nodefactor.riskg.O1                   NA   56.315       NA  7.608
## nodefactor.riskg.O2                   NA   55.056       NA  7.717
## nodefactor.riskg.O3                   NA   55.828       NA  7.606
## nodefactor.riskg.O4                   NA   55.470       NA  7.675
## nodefactor.riskg.Y1                   NA  188.548       NA 14.925
## nodefactor.riskg.Y2                   NA  181.348       NA 14.737
## nodefactor.riskg.Y3                   NA  178.944       NA 14.599
## nodefactor.race..wa.B             75.591   75.536   -0.001  8.877
## nodefactor.race..wa.H            149.174  148.884   -0.002 13.296
## nodefactor.region.EW                  NA  103.579       NA 10.682
## nodefactor.region.OW                  NA  317.762       NA 20.744
## nodematch.race..wa.B               2.540    2.510   -0.012  1.551
## nodematch.race..wa.H              13.269   13.263    0.000  3.668
## nodematch.race..wa.O             286.880  286.938    0.000 17.095
## nodematch.region                      NA  208.381       NA 14.491
## absdiff.sqrt.age                      NA  550.698       NA 30.909
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst4, type="formation")

plot(dx_inst4, type="duration")

plot(dx_inst4, type="dissolution")

Model 5

(dx_inst5 <- netdx(est.i.buildup.bal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            479.159  479.226    0.000 21.865
## nodefactor.deg.main.deg.pers.0.1 172.310  172.284    0.000 14.375
## nodefactor.deg.main.deg.pers.0.2  36.371   36.335   -0.001  6.196
## nodefactor.deg.main.deg.pers.1.0  38.033   38.049    0.000  6.273
## nodefactor.deg.main.deg.pers.1.1 135.538  135.660    0.001 12.413
## nodefactor.deg.main.deg.pers.1.2 145.388  145.092   -0.002 13.129
## nodefactor.riskg.O1                   NA   57.219       NA  7.756
## nodefactor.riskg.O2                   NA   55.557       NA  7.698
## nodefactor.riskg.O3                   NA   56.063       NA  7.811
## nodefactor.riskg.O4                   NA   54.728       NA  7.668
## nodefactor.riskg.Y1                   NA  187.212       NA 14.888
## nodefactor.riskg.Y2                   NA  182.947       NA 14.702
## nodefactor.riskg.Y3                   NA  178.344       NA 14.423
## nodefactor.race..wa.B             75.591   75.533   -0.001  8.941
## nodefactor.race..wa.H            149.174  149.136    0.000 13.425
## nodefactor.region.EW              83.501   83.440   -0.001  9.630
## nodefactor.region.OW             242.486  242.575    0.000 17.163
## nodematch.race..wa.B               2.540    2.540    0.000  1.597
## nodematch.race..wa.H              13.269   13.265    0.000  3.672
## nodematch.race..wa.O             286.880  287.020    0.000 16.926
## nodematch.region                      NA  242.945       NA 15.656
## absdiff.sqrt.age                      NA  551.233       NA 30.994
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst5, type="formation")

plot(dx_inst5, type="duration")

plot(dx_inst5, type="dissolution")

Model 6

(dx_inst6 <- netdx(est.i.buildup.bal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            479.159  478.735   -0.001 21.832
## nodefactor.deg.main.deg.pers.0.1 172.310  172.227    0.000 14.199
## nodefactor.deg.main.deg.pers.0.2  36.371   36.433    0.002  6.166
## nodefactor.deg.main.deg.pers.1.0  38.033   37.929   -0.003  6.318
## nodefactor.deg.main.deg.pers.1.1 135.538  135.411   -0.001 12.315
## nodefactor.deg.main.deg.pers.1.2 145.388  145.204   -0.001 12.912
## nodefactor.riskg.O1                   NA   52.898       NA  7.572
## nodefactor.riskg.O2                   NA   51.588       NA  7.583
## nodefactor.riskg.O3                   NA   52.599       NA  7.670
## nodefactor.riskg.O4                   NA   51.127       NA  7.483
## nodefactor.riskg.Y1                   NA  190.007       NA 15.087
## nodefactor.riskg.Y2                   NA  187.080       NA 14.936
## nodefactor.riskg.Y3                   NA  181.883       NA 14.776
## nodefactor.race..wa.B             75.591   75.437   -0.002  9.048
## nodefactor.race..wa.H            149.174  149.162    0.000 13.259
## nodefactor.region.EW              83.501   83.406   -0.001  9.491
## nodefactor.region.OW             242.486  242.423    0.000 17.565
## nodematch.race..wa.B               2.540    2.536   -0.002  1.579
## nodematch.race..wa.H              13.269   13.292    0.002  3.637
## nodematch.race..wa.O             286.880  286.565   -0.001 16.870
## nodematch.region                      NA  242.486       NA 15.608
## absdiff.sqrt.age                 380.500  380.491    0.000 22.595
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst6, type="formation")

plot(dx_inst6, type="duration")

plot(dx_inst6, type="dissolution")

Model 7

(dx_inst7 <- netdx(est.i.buildup.bal[[7]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            479.159  240.146   -0.499 15.423
## nodefactor.deg.main.deg.pers.0.1 172.310   74.549   -0.567  9.323
## nodefactor.deg.main.deg.pers.0.2  36.371   23.953   -0.341  4.991
## nodefactor.deg.main.deg.pers.1.0  38.033   34.338   -0.097  6.017
## nodefactor.deg.main.deg.pers.1.1 135.538   57.343   -0.577  7.922
## nodefactor.deg.main.deg.pers.1.2 145.388   62.023   -0.573  8.397
## nodefactor.riskg.O2                0.401    0.000   -1.000  0.000
## nodefactor.riskg.O3                0.401    0.000   -1.000  0.000
## nodefactor.riskg.O4                6.856    6.884    0.004  2.633
## nodefactor.riskg.Y1              109.513   98.987   -0.096 10.354
## nodefactor.riskg.Y2                1.349    1.345   -0.003  1.161
## nodefactor.riskg.Y3                8.202    8.189   -0.002  2.830
## nodefactor.riskg.Y4               70.786   67.042   -0.053  8.296
## nodefactor.race..wa.B             75.591   39.151   -0.482  6.422
## nodefactor.race..wa.H            149.174   66.343   -0.555  8.651
## nodefactor.region.EW              83.501   43.250   -0.482  6.823
## nodefactor.region.OW             242.486  131.260   -0.459 12.845
## nodematch.race..wa.B               2.540    1.413   -0.444  1.191
## nodematch.race..wa.H              13.269    5.067   -0.618  2.227
## nodematch.race..wa.O             286.880  148.018   -0.484 12.155
## absdiff.sqrt.age                 380.500  301.563   -0.207 24.945
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst7, type="formation")

plot(dx_inst7, type="duration")

plot(dx_inst7, type="dissolution")

Model 8

(dx_inst8 <- netdx(est.i.buildup.bal[[8]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))
## 
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## 
## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
## 
## Formation Diagnostics
## ----------------------- 
##                                   Target Sim Mean Pct Diff Sim SD
## edges                            479.159  382.651   -0.201 19.588
## nodefactor.deg.main.deg.pers.0.1 172.310  120.857   -0.299 11.710
## nodefactor.deg.main.deg.pers.0.2  36.371   34.694   -0.046  6.006
## nodefactor.deg.main.deg.pers.1.0  38.033   38.189    0.004  6.302
## nodefactor.deg.main.deg.pers.1.1 135.538   94.401   -0.304 10.191
## nodefactor.deg.main.deg.pers.1.2 145.388  103.699   -0.287 10.866
## nodefactor.riskg.O1                0.401    0.000   -1.000  0.000
## nodefactor.riskg.O2                0.401    0.000   -1.000  0.010
## nodefactor.riskg.O3                6.856    6.871    0.002  2.631
## nodefactor.riskg.O4              109.513   97.724   -0.108 11.039
## nodefactor.riskg.Y1                1.349    1.368    0.014  1.172
## nodefactor.riskg.Y2                8.202    8.130   -0.009  2.874
## nodefactor.riskg.Y3               70.786   70.442   -0.005  8.709
## nodefactor.race..wa.B             75.591   60.147   -0.204  8.125
## nodefactor.race..wa.H            149.174  108.233   -0.274 11.155
## nodefactor.region.EW              83.501   66.050   -0.209  9.606
## nodefactor.region.OW             242.486  204.241   -0.158 18.745
## nodematch.race..wa.B               2.540    2.326   -0.084  1.526
## nodematch.race..wa.H              13.269    8.341   -0.371  2.879
## nodematch.race..wa.O             286.880  235.903   -0.178 15.499
## nodematch.region                 383.327  289.268   -0.245 17.058
## absdiff.sqrt.age                 380.500  323.774   -0.149 21.072
## nodematch.role.class.I                NA    0.000       NA  0.000
## nodematch.role.class.R                NA    0.000       NA  0.000
## 
## Dissolution Diagnostics
## ----------------------- 
##                Target Sim Mean Pct Diff Sim SD
## Edge Duration       1        1        0      0
## Pct Edges Diss      1        1        0      0
plot(dx_inst8, type="formation")

plot(dx_inst8, type="duration")

plot(dx_inst8, type="dissolution")